diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3387cba --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +*.npy \ No newline at end of file diff --git a/ML_LES/Comparison/.ipynb_checkpoints/Visualization-checkpoint.ipynb b/ML_LES/Comparison/.ipynb_checkpoints/Visualization-checkpoint.ipynb new file mode 100755 index 0000000..6cfe578 --- /dev/null +++ b/ML_LES/Comparison/.ipynb_checkpoints/Visualization-checkpoint.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#load DSM data\n", + "loc_1 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_1.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_2 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_2.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_3 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_3.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_4 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_4.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_5 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_5.csv',delimiter=',',skiprows=1)[:,:10]\n", + "\n", + "# Add all and average\n", + "dsm_data = 0.5*(loc_1+loc_2+loc_3+loc_4+loc_5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#load ML data\n", + "loc_1 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_1.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_2 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_2.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_3 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_3.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_4 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_4.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_5 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_5.csv',delimiter=',',skiprows=1)[:,:10]\n", + "\n", + "# Add all and average\n", + "ml_data = 0.5*(loc_1+loc_2+loc_3+loc_4+loc_5)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#load UNS data\n", + "loc_1 = np.loadtxt('../UNS_Validation/Loc_1.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_2 = np.loadtxt('../UNS_Validation/Loc_2.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_3 = np.loadtxt('../UNS_Validation/Loc_3.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_4 = np.loadtxt('../UNS_Validation/Loc_4.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_5 = np.loadtxt('../UNS_Validation/Loc_5.csv',delimiter=',',skiprows=1)[:,:10]\n", + "\n", + "# Add all and average\n", + "uns_data = 0.5*(loc_1+loc_2+loc_3+loc_4+loc_5)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make plots - Reynolds stresses\n", + "plt.figure(figsize=(12,10))\n", + "plt.plot(dsm_data[:,4],label='DSM',linewidth=4)\n", + "plt.plot(ml_data[:,4],'ro',markersize=3,label='ML')\n", + "plt.plot(uns_data[:,4],'bo',markersize=3,label='UNS')\n", + "plt.legend()\n", + "plt.title('Rxx')\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "plt.plot(dsm_data[:,5],label='DSM')\n", + "plt.plot(ml_data[:,5],'ro',markersize=3,label='ML')\n", + "plt.plot(uns_data[:,5],'bo',markersize=3,label='UNS')\n", + "plt.legend()\n", + "plt.title('Ryy')\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "plt.plot(dsm_data[:,6],label='DSM')\n", + "plt.plot(ml_data[:,6],'ro',markersize=3,label='ML')\n", + "plt.plot(uns_data[:,6],'bo',markersize=3,label='UNS')\n", + "plt.legend()\n", + "plt.title('Rzz')\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "plt.plot(dsm_data[:,7],label='DSM')\n", + "plt.plot(ml_data[:,7],'ro',markersize=3,label='ML')\n", + "plt.plot(uns_data[:,7],'bo',markersize=3,label='UNS')\n", + "plt.legend()\n", + "plt.title('Rxy')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error due to ML: 0.008301148499999998 Error due to no model: 0.0185938405\n", + "Error due to ML: 0.0026460942454999995 Error due to no model: 0.0048548652905\n", + "Error due to ML: 0.0025481001 Error due to no model: 0.00524449995\n", + "Error due to ML: 0.0035513259109999995 Error due to no model: 0.004304335090550001\n" + ] + } + ], + "source": [ + "# Quantitative - Rxx\n", + "ml_error = np.sum(np.abs(dsm_data[:,4]-ml_data[:,4]))\n", + "uns_error = np.sum(np.abs(dsm_data[:,4]-uns_data[:,4]))\n", + "print('Error due to ML:',ml_error, ' Error due to no model:', uns_error)\n", + "\n", + "# Quantitative - Ryy\n", + "ml_error = np.sum(np.abs(dsm_data[:,5]-ml_data[:,5]))\n", + "uns_error = np.sum(np.abs(dsm_data[:,5]-uns_data[:,5]))\n", + "print('Error due to ML:',ml_error, ' Error due to no model:', uns_error)\n", + "\n", + "# Quantitative - Rzz\n", + "ml_error = np.sum(np.abs(dsm_data[:,6]-ml_data[:,6]))\n", + "uns_error = np.sum(np.abs(dsm_data[:,6]-uns_data[:,6]))\n", + "print('Error due to ML:',ml_error, ' Error due to no model:', uns_error)\n", + "\n", + "# Quantitative - Rxy - important\n", + "ml_error = np.sum(np.abs(dsm_data[:,7]-ml_data[:,7]))\n", + "uns_error = np.sum(np.abs(dsm_data[:,7]-uns_data[:,7]))\n", + "print('Error due to ML:',ml_error, ' Error due to no model:', uns_error)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ML_LES/Comparison/Visualization.ipynb b/ML_LES/Comparison/Visualization.ipynb new file mode 100755 index 0000000..6cfe578 --- /dev/null +++ b/ML_LES/Comparison/Visualization.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#load DSM data\n", + "loc_1 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_1.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_2 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_2.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_3 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_3.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_4 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_4.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_5 = np.loadtxt('../Dyn_Smag/D_SMA_Case/Loc_5.csv',delimiter=',',skiprows=1)[:,:10]\n", + "\n", + "# Add all and average\n", + "dsm_data = 0.5*(loc_1+loc_2+loc_3+loc_4+loc_5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#load ML data\n", + "loc_1 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_1.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_2 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_2.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_3 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_3.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_4 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_4.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_5 = np.loadtxt('../ML_LES/ML_LES_Case/Loc_5.csv',delimiter=',',skiprows=1)[:,:10]\n", + "\n", + "# Add all and average\n", + "ml_data = 0.5*(loc_1+loc_2+loc_3+loc_4+loc_5)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#load UNS data\n", + "loc_1 = np.loadtxt('../UNS_Validation/Loc_1.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_2 = np.loadtxt('../UNS_Validation/Loc_2.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_3 = np.loadtxt('../UNS_Validation/Loc_3.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_4 = np.loadtxt('../UNS_Validation/Loc_4.csv',delimiter=',',skiprows=1)[:,:10]\n", + "loc_5 = np.loadtxt('../UNS_Validation/Loc_5.csv',delimiter=',',skiprows=1)[:,:10]\n", + "\n", + "# Add all and average\n", + "uns_data = 0.5*(loc_1+loc_2+loc_3+loc_4+loc_5)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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Qnp19wOukf7N9D6+v/JaXlm9m4w+7SUl0MeiU1lzUK5W+7VvicpVbke+A9Gx7GP84P42xT+/j6yFDafviTOc5CgoaXPmLJpPWoNxc5++U3+/8mZt78H+3wixLWe74LbfcwuDBgznvvPNKjqWmprJ+/XrmzZvHvHnzyMzM5IUXXiAzM/PgOiQiIiJ1i88HV19dfr30IUOqHdK/3raHN1Z9y5urvmXVlp0A9GjTnAm/78x5XVrTtILa84NxdqdWtGlxCJOLTmdC4qwGO6lUQb0GZWQ4I+nFI+oZGQffZsuWLdmxY0epY9u3by81KbRDhw5069aN559/vtR1ycnJnHvuuZx77rkcddRRvPrqqwrqIiIi9YnPB/37O5MvQ7lc1apLX/71Dm59bU1JOO+a2oxbzj2ZQZ1bc2yLQ6LZ47DcLsOo09tx2//tITvjLFq8GyyyaGCTSg96Z1KJzONxyl3uuCN6ZS9NmjShdevWzJs3D3BC+ttvv02/fv1KXfe3v/2Ne++9t+T98uXL2bp1KwCBQICVK1fStm3bg++QiIiI1A3FI+llQ7oxMHlylYPI6i07GfnEUrb/WsBfB53MguwzeW1cP0b3P75WQnqxi3sdS7NGiawqalT6RAOaVKoR9Rrm8UT/S9+MGTO47rrruPHGGwG49dZbOf7440td06lTJ3r06MHy5csB+OGHH7jmmmvIz88HoE+fPowbNy66HRMREZHYiDSSXs310jf+sIsRTyzl0JREnh/j4ZjmjSq/qYY0Tk5g2KltePDzvpyROBtXA5xUqqAeh9LS0nj//ffLHZ82bVqp9y+//HLJ64EDBzJw4MCa7pqIiIjEwqRJBx3Sv9m+h2FTl+AyhplXnxrTkF68al6X3u157Ng08gYMoc87LzgnCwujM/EvDiioi4iIiMQznw9mzy59rJoh/bud+7hs6mL2FQaYNbov7Q+veK8Vn8/5brB+PSQnQ34+HHEEpKUd+FzP4jYXL4bvv3fmjiYkJHPmqM685mpFb5yNdggEoGXLSlqrHxTURUREROLZpEmlV3ipZkj3fb6NP89awa59hTx9TV9ObnVo2Ou8Xrj/ftixI3yZ+Lp1zgaojz4KHTrAYYfBVVdF7obP58wLXbsWPvssfJtFRfDeY6kc2qEb1rgwNuBMjN22rUrPFu8U1KvJWhtxicT6wJZdb1VERETqLq8XXnut9LHBg6sU0gv9Ae5/7zMeyf2c9i0bM3Vkr7C7iXq9cNdd8NVXVe/Whg3On0uXOhujnnUWdO/ulJd//LEzAl88al4Zaw0vfXYFk13LuNb1GCZaS+nFAQX1akhJSWHbtm20bNmyXoZ1ay3btm0jJeXAdhQTERGRWuTzwbXXlk67bneVlmHc+vNexj2znOVf/8wlvVK59bedaJxcPhYOHw5PP31w3dy40fk5OIZxgYf5umNXJvb/5GAbixsK6tWQmprK5s2b+fHHH2PdlRqTkpJCampqrLshIiIilZk0ydlVsZgx8MgjlRaI523azjUz8igoCvDgpd25oOvR5a7x+WD8eKeUpTIdOjh7xlRnxL0iLVrAoYc67e3/DuIMkN6z7hqO//RasqZn1s6W7zGmoF4NiYmJpTYWEhEREYmJcBNIq1Dysju/iOuf/ZhDGyXy5BW9Oe6IJuWu8Xph7NjyG5sWa9UKTjyx/MTR0Ammu3bB5s1Vf5xWrZyA/qc/7X+E8v0wWFyMtQ/TOX8Nngaw8ouCuoiIiEi8KTuBtIo7j056+1O+/WUfL445LWJIHzMmfO14164V75vk8cArr5Ru6/77nUmmoYpHzNu0qXiVmKws6NzZ2cNp7drio4YAbmbY4Xha1v99OxXURUREROJJuAmkF1xQ6ejy0i+3M8P3FVee3o6ebQ8L22ykkJ6dDRMnVq+bWVnOT/FI+9atFa8CE47HA1OnQr9+pb+XfGePghuGOUm+Ho+q1/+vIiIiIiL1Rbg0XYUJpPsK/Yx/aSWphzXiprNPqlKz4JS9T5lS/ZAeqnikfcmS6oX00PsnT3Z+aQBOB9/iXHz5PZyNj+oxBXURERGReODzOUXboWm6ihNIH5i7gS9++pW7f9+53OouOTkwenT5kO5yVWs59hpVPDrvMBSQyAwur/fLNCqoi4iIiMSDsnXpUKUJpCs3/4z3gy+4uGcqZ3Q4otS5nByn2bLS0mDhwroR0ouNGAGJCQHAYnHxZGAkvlXl6+zrEwV1ERERkbouXF16FSaQ7iv0c+Pzn3BEk2T+fl5auSbDhfTERKcuvK6Vfns8MLLbx0AAMBSSQO5L9XuHUgV1ERERkbosXAG5MRUvwQLkF/n547Mfs/GH3Uy8qAvNDkksOVdc7lJWejrMn1/3Qnqx3gMOw4mvlgBufqb8Tqr1iYK6iIiISF0VqS69kuLxfYV+smZ8xJy133P74E70P3F/yUukcpe6HtIBtjU/HmdCqbMB0n3vdsLnXRXTPtUkBXURERGRuuoA6tJ/zS/iyieX8cGGH5l4YWdGeNqVnPP54J57yt+TmAgTJkSpzzUoIwPcxqlTB0MRbmY8XhjjXtUcBXURERGRuugA6tJ/2VfIyCeWsnTTdu67pBt/6N2m5JzP52weVHZ1l3gYSS/m8cA/Rn+KmyJKJpUu74LPF+ue1QwFdREREZG6xueDa6+tVl36L/sKuXzqElZ88zP/u7Q7Q7ofU6q5/v1Dd/h0xFNIL5b9QEc6py6mZFJpEeTO+CrW3aoRCuoiIiIidc2kSeD3739fhbr022avYfXWX3h0eE/O7dy6XHOFZSpE3O74KHcpq1GSm66HbSJ0UmnL79bEuFc1Q0FdREREpC4JV/JSSV36O2u+4+XlW7gu43jOSjuqXHOvvlr6+iruk1Rn/XL0KbjwAwZDgI/pHusu1QgFdREREZG6IlzJi9tdYV36T7vz+evLq+h09KGMG9Ch1LlwyzBWYXC+zju7z24SQuvU3ziqXtapK6iLiIiI1BXhSl4qGPq21vLXl1exK7+I+/7QjaSE/dEu0jKMVdjMtM4bbnK5gmmYYJ16UZElNzfWvYo+BXURERGRusDng9mzSx+rJFW/vHwL7679npvOPpETj2pacryiXUcr2cw0LjQZeBaXup8lkUIMftwuS0ZGrHsVfQrqIiIiInVB2TXTK1mKcevPe7lt9hr6tGvBVf2OKzker7uOVovHw6Lzhwe3PQLjL4JV9W/jIwV1ERERkVjLySk/4/OCCyKm6kDAkv3iSvzWcu/FXXG7TMkSjPG662h1/fBdKkUkYHFThJvcl7bFuktRp6AuIiIiEkvhiskrmUA6w7eJhRt/4m/ndaRNy0NKQvoHH5S/Nl52Ha2usy45giQKcFNIEoVkXNgy1l2KuoRYd0BERESkwfL54J57Sh+rZALp83nf8K/X1zLg5CO5rI+z8+j48eXXSQdnJH3ChPo1kl7s/D/3ZOhL99Piy0ZceHkrPFlDYt2lqNOIuoiIiEgs+Hxw9dWll2IEuPnmiBNIZ/g2kf3iSvqdcDgPX9aDxYtNxJH07Oz6V+4SyixezORltzDk26fI/e9yfN76V6OuEXURERGR2lZcq1J2GDw9HSZODHuL94PPuevNTzmr45H877IezHjSzdixpeefFpsyJf6XYKxUbi7LinpyNnMoKEoiaZxlbuf69cVEI+oiIiIitS1crYrbHbaY3FrLA+9t4K43P+W8Lq2ZPLwnH+dFDunZ2Q0gpANkZJDrGkABSfhJoMCfUO/WUldQFxEREalNOTnla1Ui1KVba5n49nrue+8zLuyRyoNDu5O31MXVV0cO6REG5Osfj4dmOb/B5fLjdgVISjb1bi11lb6IiIiI1Bavt/zkUYBHHw07DP74wi95dP7nDO/bhtsvOIWpU03YkXRjIjZRr/W4Mo2WX+fR19+DVs0axbo7UaegLiIiIlIbvF4YM6b85NEItSoffv4Td7/1KQM7tWLQkadw4YWm3FLr0HBDOkDrZimc+OMm3p7bm8KAZfp0w9y59adOXaUvIiIiIjXN54OxY8OH9DC1Klt/3ssfn/mYdi0PodOurpxxhkJ6OMesW0HmnHUUFrnwBwwF+bZe1akrqIuIiIjUtEmTyterDBkSNqTvK/QzduZH5BcFGEBf/jQuIWw9ekMP6QDJixZwZuD9/RsfuYvqVZ26grqIiIhITfJ64bXXSh9zuSLuPHrb7DV8snknmaYv/7g5pdwgfPHtDT2kA5CRQe/Ej/gvfybTlcv9f/663pS9gGrURURERGqOzwfXXlu65MUYmDw5bCH1s0u/5rll39Brbw8emtQsbEgfMsTJ+PUpkB4wj4c/XfYC0546Bz+JLHjI0HlI/flvoxF1ERERkZoyaRL4/fvfV1Cv8vHXO7j1tTU0/qQ7Lz3YqlxId7mcjYxeeaX+BNFo+KyoNwU2walRL0A16iIiIiJSiXAlL4MHhw3pm3fsIeupj9izqCNr324NmFLn09Jg4UKVuoTT49RC3C4/buMnKcGvGnURERERqUC4khe3O2xd+s69hVz55DK2Lm7FlvltKRvSExNh6lSNokdyfpPlvEcmt/NP5tpMPPhi3aWoUVAXERERibbx48uXvITZebSgKMDYmR+x7IVj2Pp6J8qG9PR0mD9fIb0ix61ZRr/AIs6088gtPB3fjA2x7lLUaDKpiIiISDTl5MAHH5Q+FqbkxVrL+JdX8tr9qexZewyRQrpULDFzAAvv/4BB/rcpsEkkPWmYO6J+fLnRiLqIiIhItHi9cM89pY8ZE7bk5c8PfcPDfwkf0hMTYcKEGuxnPXJYZn+u6/xv8k0yfhIoKHLXmwmlCuoiIiIi0RCuLh3g5pvLDe+O/tt2HvhTKgWbW5ZrRuUu1dMoyU3bY78l0eXH7bIkJVFvJpSq9EVEREQkGsrWpYMzkl5m99GcO3fhvas5zih66ZH0YcNg5sya7Wa94/Px8tsjyfM/yIKETDLuvxiPp3OsexUVGlEXEREROVjh6tKHDCkX0rPG5TPp700IF9KzsxXSD0huLolFhbgJOF+UPv441j2KGo2oi4iIiByMKtal3/CXIh57OKn4glKXRtgDSaoiI4NF7jMYWPSmJpOGY4wZaIxZb4zZaIwZH+Z8sjFmVvD8EmNMu5BztwSPrzfGnFNZm8aY9sE2NgbbTAoev88YsyL485kx5ueQe/wh52ZH45lFRERE8HphzJhK69KnTLE88F938N3+kO5yKaQfNI+HKec+xD40mbQcY4wbeBg4F0gDLjXGpJW57Cpgh7X2BOA+YGLw3jRgKNAJGAg8YoxxV9LmROC+YFs7gm1jrf2ztbabtbYb8BDwcsjn7y0+Z6294GCfWURERASfD8aOLR/Sy9Sl5+Q4Wd6xP6Snp2u30Wjp9LujIAHc7vo1mTQaI+p9gI3W2i+stQXAc8DgMtcMBqYHX78IZBpjTPD4c9bafGvtl8DGYHth2wzeMyDYBsE2h4Tp06XAs1F4NhEREZHwJk2CQKD0sZC6dJ8P+veHSZOKg3zpkK6VXaInI93FGZnPcaNnDnPvX1Vv/rtGo0b9GOCbkPebgVMjXWOtLTLG7ARaBo8vLnPvMcHX4dpsCfxsrS0Kcz0Axpi2QHtgXsjhFGNMHlAETLDWvhruQYwxWUAWQJs2bSI8roiIiDR4Xi+89lrpYy5XSV261+sMtgcCoSHdAkZrpNeAdp+t5O25o1ju703u4gEAeLLif+WX+jiZdCjworU2dH2kttbaLcaY44B5xphV1trPy95orfUCXoBevXrZsudFREREwtalGwOTJ4PHE3LaCeYO53V6uhPS68uIb11xxEc+Fhf14hzepaAoiaRxlrmd4/+/czRKX7YAx4a8Tw0eC3uNMSYBaAZsq+DeSMe3Ac2DbUT6rKGUKXux1m4J/vkFkAt0r+rDiYiIiJQIV5cesmxL6QxfOqRnZ6vcpaYkZg4g1zWAApKcCaX+hHoxoTQaQX0Z0CG4GksSTlAuu7LKbGBk8PVFwDzrfM2cDQwNrgrTHugALI3UZvCe94NtEGyz5PdOxpiTgcMAX8ixw4wxycHXhwOnA2uj8NwiIiLS0ISrSx88OExIL2YxxjBlSrkl1SWaPB7WXdwDtzvg7E6abOrFhNKDLr1k5AYAACAASURBVH0J1pyPA94B3MAT1to1xpjbgTxr7WzgceApY8xGYDtO8CZ43fM4wbkIuK64ZCVcm8GPzAGeM8b8G/g42HaxoTiTU0P/iXQEphhjAjhfTCZYaxXURUREpHoqqEsvH9JtyenJk7WyS23oeuLPtOvzX5p2vZSMEW3rxW8ujC27pJAATo16Xl5erLshIiIidYHPB2ec4ex8WSxY8uIlK+xI+gknBpgxzV0vAmOd5/NRmDEAU1iAOyUZM3du3NQYGWM+stb2CncuKhseiYiIiNRrkyZVK6QbFwrptSk3F3dRAQk2gC+/B3fflo/PV/ltdV19XPVFREREJHrClbwMHhwmpDsvjLE8OtmlkF6bMjKwSUks2NeDcwLvUvBeI5IWQBwNrIelEXURERGRSMLNEHW7yWn8EKNHl9+UtMlRe3h/vlVNem3zeNg0azZ3tbuBfFcK/oChoIC4X/lFQV1EREQknAhLMeZkLmPS06llLraAZeZ0N/3PcNdmLyWoRWZ/9pxUQKIpclZ+SSLuV35R6YuIiIhIOGGWYvR2/R+T3i27HYsFY7nxtt0MPufQ2uuflNL8kzzennsVywOP8IE7k4z7L8bjie/dSRXURURERMoKU5eew91MWjG2zIWWpNTtjP7Lbv5zQ9va65+UY+bPJ8lfyOn2Q063S2BbIyC+g7pKX0RERERC+Xxw7bUlJS8++tKf95lEDvt3GwWwJB+7jYtv3ch917eJSVclREYGRQmJFLlc1Iu6FzSiLiIiIlJayFKMTkjPpZAkyoZ03JbjB33JA0O74XaZsE1JLfJ4ePQfU0hetIAxt14V38u9BCmoi4iIiBTz+WD27JK347k7bEg/6qRdJPRZzYxbTqZlk+Ra76aEt6dnH+79/ES2v9+XM4n/rK6gLiIiIgJOSL/6aggE8NGX8dzNB/Qvd9nZl+5gfRsf/zg/jZ5tW8SgoxLJzk2HsuXpE/mndapf4n0ddQV1EREREZ8P+veHwsIy5S4QOpp+y52/Mmv3YgZ1asWo09vFpKsS2eZ1TbBFBj+GgnxLbq6J66CuyaQiIiIikyZBYSFQttxlf0gfd0MR71kfbVocwsQLu2CM6tLrmrOPWksj9uGmkKTAXjJarop1lw6KRtRFRESkYQupS8/hrrDlLjdnWzafkMfOrwqZPqoPTVMSa7uXUgUD97zNO9zEQtLJcC3As+084nmJRo2oi4iISMMW3Ngoh7uCSzBC6Ej6lClwVOZnfPj5Nv495BQ6ttamRnVVwoAz6ZWQR47rHjzJy+N+iUYFdREREWm4ghsbOSF9PGXLXbKzoUP/H3ho3kYu6ZXKxb2OjVlXpXKH9D+DYUPvZMmoG+J/JikqfREREZGGKrixkddeVWYk3QKG7Gy4/pa9nPfgCk5u1ZTbB58Sw85KVaQkulnTthPvn3YeHk/HWHfnoCmoi4iISMM0fjxe/5WM4VH2j6TvD+l33BngkinL8fstk4f3JCXRHdv+SpU0PySRnXsKY92NqFDpi4iIiDQ8OTl4PziRMTyKxcX+chdDejpMnAh3vbmOFd/8zD0Xd6H94Y1j2VuphuaNkli/MpG773Z+aRLPNKIuIiIiDYvXi3fSjjIh3QKQmGiYMAFeX7mVaR9u4qp+7Rl4SuuYdleqp+jbw3j9/hN5PRD/mx5pRF1EREQaDq8X7+g8xjC5zEg6pKUZ5s+HI0/YTc6LK+nRpjnjzz05dn2VA5KQl0+gEPx+KCiA3NxY9+jAKaiLiIhIwxAxpFtcLsPUqdCtp59rZy4nOdHN/y7rQaJbUSmu+Hz8bcFfSCHf2fQowR/XKzSq9EVERETqP6+XnNHbmVRu4ii4XIbJk6FvX8tNL6zmsx92Mf3KPhzdvFEseywHIjeXfoGFzCWTXDOAjCtPwuMZEeteHTAFdREREanffD6Gj07h6TCbGaW128PUZxrj8cCsZd/w0vLN/CmzA+knHhGbvsrBycjAn5hE74Kl9E35BDNibqx7dFD0+xwRERGp13JGbuVpLg++Kx5JtyS6bUlIX7N1J/94bQ1ndDic6zM7xLC3clA8Ht578Gn+e8Zwdsx+M35nkQYpqIuIiEi9lXPqPCZt+F3w3f5yl/SuvzB/gQuPB37ZV8i1Ty+nxSFJ3P+HbrhdJmJ7UvcVntqXRzyXsL1Lz1h35aApqIuIiEi9NDwtj0lLz6RsTfqwPp8xf0UzPB7wByx/ef4TtuzYy/8u607LJsmx7LJEQbNGieRvac5997rjfh11BXURERGpd3KGb+bpdcUjqvvLXbLPXsHMJScBYK3ljtfXMmft9/z9vI70atciVt2VKPpqbQrfP9eXR//TiMzM+N70SEFdRERE6pWcHJj09NHBd/tH0rP75DLxne4l1z2+8EumfbiJq/u154rT29d+R6VGrM5LxvoNgYDROuoiIiIidUVODkyaZClX7nLEu0xcMqDkutdXbuXfb6zjvM6t+eugjjHpq9SMszINbpcft/HH/TrqCuoiIiJSL3i9MGlSIPgupNyFScx87dCS65Z+uZ0bZ31C73aH8Z9LuuLS5NF6JTNpGe+Rye38g7k2Ew/xW/uiddRFREQk7nm9MGZMgLIj6dlMYuKUFiXL9G38YRfXzMjj2BaNeGxEL1IS3THrs9QM9wfz6RdYRIZdAH63U/sSp8s0akRdRERE4poT0sHa4pDuSGc+E7N3QFYWAD/8so+RTywj0e1i2pV9aH5IUox6LDUqI4OihET8LjckJRHPtS8K6iIiIhK3fD4YO9ZZwaXUZkYUMKHb8zBxIgC784sYNX0ZO/YU8OQVvTm2xSGx7LbUJI+Hv4+7n5d/lwVz58btaDooqIuIiEic8vng6qshECgO6Y401jDflYnnEWc30kJ/gOueXs66b3fx8LAedE5tFqMeS235vlN3nhkwLK5DOiioi4iISBzyeqFfP1i71oYctbjwM5UsPJNHgMeDtZa/v7Ka+Z/9yJ1DTuHMk46MWZ+l9jRrlMjXaxtx993xvY66JpOKiIhIXHGWYCx+VzySbjEEmMxYPNlnlNSlPzRvI7PyvuH6AScwtE+bWHRXYmDX1834eEo7PrFOmXq8VsBoRF1ERETixvDhoSG9mBPSH2UMWemfldSlv5D3Df+d8xkX9kjlz785sdb7KrHzw/qmWL/B7yeuNz1SUBcREZE6z+uFdu3g6aeheOnF4omjLvxOSHc/CRMmAPDBZz9yy8ur6HfC4dz9+84Yo7XSG5IuvQswbovbbeN64ReVvoiIiEidNnx4cUAHJ5zvXyc9nflM4BY8Zgk88ih4PKzZupOxMz/ihCObMHl4D5ISNC7Z0JydnMfuU1bSvNswzs86Pi7LXkBBXUREROoonw/Gj4cPPgg9uj+kD+MpZjLSOXxzNmRlseXnvVz55DIObZTItCv70DQlsba7LbHm85Ex7lL65xfg+nQCrqy5QHwmdX3FFBERkTqneFWX0iHdKXUxBMhmwv6Qnp0NEyeyc08hVzyxlL2FfqZd2YdWzVJi0HOJudxcXAWFJNgAFMZxgToaURcREZE6Zv9Oo6FHnTdd+ZjJXIeHxc7hKVMgK4v8Ij9ZT+WxaduvTB/Vh5NaNa31fksdkZGBTUrCn5+PSUyM3wJ1NKIuIiIidUhODoweHS6kW7KZwAp67g/p2U65SyBguemFlSz5cjv3XtyV044/PAY9lzrD42H77Df57xnDmfvQM/G5LmOQRtRFRESkTig9abRYyNKLTN1/OFjuAjDxnU/5v0+2kjPwZAZ3O6bW+it1V+P+Z/DI3F+5OL8rH93tDKrHY15XUBcREZGYCj9p1BlSd+FnMmNLh/RguQvA9A83MWX+Fwzv24Yx/Y+rvU5LnZaS6ML/XQseuO9oAv743fRIpS8iIiISM+EnjTrSmc9Czig/kh4M6TN8m7h19hrO6ngUt/22k9ZKlxLGGOzWIygqiu9NjxTURUREJCaK69EDgdCjTj36MJ5iPmfur0eHUuUu3g8+55+vreE3aUfx8LDuJLgVaaQ0T7NPSDSFuF3xu+mRSl9ERESk1kWqR3cmjU5kIn8tfSpY7mKt5aF5G/nvnM84r0tr7v9DNxIV0qUsn4+Zrw1jmb8nCxIyybj/YjyezrHuVbUpqIuIiEitCV+P7nARYHLZSaNQUu5ireWed9bzSO7nXNgjlUkXdcHtUrmLhJGbS0JRIf34kH52CWxrBMRfUNdXUBEREakVOTlw+unhQrol/dAVLKRf+JA+cSLWWm5/fS2P5H7OsFPbcI9CulQkIwN/YiJFxkXc1r2gEXURERGpYRWNojv16DOZ+cuI8qeC5S6BgOVvr67m2aVfM+r09vzj/I6aOCoV83iY+e8n2PnWHP58Z1b8LfcSpBF1ERERqTGRR9EBAmS3mMpMwoT0YLlLkT/ATS98wrNLv+bajOMV0qXKdvfozQN9LqKwz6mx7soBi0pQN8YMNMasN8ZsNMaMD3M+2RgzK3h+iTGmXci5W4LH1xtjzqmsTWNM+2AbG4NtJgWPX2GM+dEYsyL4c3XIPSONMRuCPyOj8cwiIiISmc8H/fvDpElldxl1GBNgCmOYuD2r/MlguUuhP8CfnlvByx9v4S+/OZHsgScrpEuVNWuUSP6W5vzrjgA+X6x7c2AOOqgbY9zAw8C5QBpwqTEmrcxlVwE7rLUnAPcBE4P3pgFDgU7AQOARY4y7kjYnAvcF29oRbLvYLGttt+DP1OBntABuBU4F+gC3GmMOO9jnFhERkfAqHkWH9K47WcQZZPFY6RPGOOUuEyeyr9DP2Jkf8caqb/nboI78MbNDzXdc6pXvNjTm++f6cve/3WRmEpdhPRoj6n2AjdbaL6y1BcBzwOAy1wwGpgdfvwhkGucr8WDgOWttvrX2S2BjsL2wbQbvGRBsg2CbQyrp3znAHGvtdmvtDmAOzpcCERERiSKvF9q1q2gUHaZkf878wtPw2A/Ln3z0UcjKYm+Bn2tm5PHeuh+4Y3AnrknXjqNSfRs/aYT1GwJ+06A3PDoG+Cbk/ebgsbDXWGuLgJ1AywrujXS8JfBzsI1wn3WhMWalMeZFY8yx1egfAMaYLGNMnjEm78cff4z8xCIiIlKiuMxl9Gj46qvw16Snw6KbXyXr3hNh7drSJ0NC+u78Iq6ctpSFG39i0kVduNzTrsb7L/XT6WcEcLv8uI2fpAR/XC78Up8mk/4f0M5a2wVn1Hx6JdeXY631Wmt7WWt7HXHEEVHvoIiISH3j9UK/fpHLXIqrWeYP8+K55/dltyEtFdJ37i1kxONLWLZpB/f/oRuX9Do2fKMiVXB2Sh7vkcnt/JO5NhMP8Vf7Eo2gvgUI/ZeUGjwW9hpjTALQDNhWwb2Rjm8DmgfbKPVZ1tpt1tr84PGpQM9q9E9ERESqweeD3/3OGUUvm72LpafDokWQ9XmOc2HZepiQkL7j1wKGTV3Mqi07efiy7gzuFvaX3yJVdtjSD+kXWMRf7V14/AvjsvYlGkF9GdAhuBpLEs7k0NllrpkNFK+2chEwz1prg8eHBleFaQ90AJZGajN4z/vBNgi2+RqAMaZ1yOddAKwLvn4HONsYc1hwEunZwWMiIiJyAIpH0V99Nfz59HT48EOYPx88Dw93itbLCgnpP+7KZ6h3MZ99vxvv5b0YeErr8teLVFPSWQModCcQcLnjdtOjg97wyFpbZIwZhxN+3cAT1to1xpjbgTxr7WzgceApY8xGYDtO8CZ43fPAWqAIuM5a6wcI12bwI3OA54wx/wY+DrYNcL0x5oJgO9uBK4Kfsd0YcwdO+Ae43Vq7/WCfW0REpKHx+ZzMHSmgh2TvYOF6hF2OXC6YPBmysvh2516GPbaEb3fu48krenP6CYfX6DNIw+E+/TSuGTGBKwq/4qyxf4jLTY+MDTctW+jVq5fNy8uLdTdERETqBK8Xxo6NXOYSkr2d9RnvuSf80i/p6TBhAng8fLN9D5dNXcyOXwt58sre9G7XokafQRqe/ve8T7djm/PA0O6x7kpExpiPrLW9wp076BF1ERERqd+8XhgzJnzuBhgyxNmjyEMFo+gAw4bBzJkAfPnTr1z22GJ+zS9i5tWn0u3Y5jXUe2nImjdK5Oc9hbHuxgGrT6u+iIiISJTlRJgHCs4o+pQp8Mor4FlVyfIv2dklIX3V5p1cMsVHflGAZ7P6KqRLjWl2SBI/71VQFxERkXrE54Pu3cPPAwVnFH3hwmCpS/GQe7i6mJDdRgHeWfMdl0zxkeR2MSurL52OblZzDyENXvNGiXy9LoW7747PnUlV+iIiIiKl5OREDuilatGh4rqYkHp0ay3eD75gwtuf0iW1OY+N6MmRTVNq7BlEAA5Z8D1rppzJP7AkJRvmzo2vOaUaURcRERFg/w6jkUJ6enrIKDpEDunFNTHz54PHQ0FRgPEvreLutz5lUOfWzMrqq5AuNc/no4l3EYGAG3/AUJBv424pdQV1ERERqXSH0WHDSnK3I1LxelpaqTS/c08hI59Yyqy8b/jjgBN4aGh3UhLdNfcgIsVyczkzMI8kCnBTSJK7KO6WUlfpi4iISANW2dro7drBLbeEjKIDDB8OTz9d/mKXC6ZOLUnzm376lVHTl/HN9j385+KuXNgzNer9F4koI4NTk27nnfzfsCAhkzP/dzEeT+dY96paFNRFREQaqMrWRs/OLpkD6vD5YHwlmxgFQ/qSL7YxeuZHGODpq/vSp73WSJda5vGw5LHn8U19kctuTqfd+fEV0kFBXUREpEGqaA5oqR1GQ2+IlOpDJo0CvPLxZrJfXMmxLQ7hiZG9aXd445p5CJFK+E/tyyNrXJx5Sg/axbozB0BBXUREpIGp1qouUHGqD9nEyFrLI7mfc8876/Ec15JHh/ek2SGJ0X8AkSpqmuJE3V374nMtdQV1ERGRBiRSeTmE7DAaunxdRak+pDbGH7DcNnsNTy3+isHdjuaei7qSlKA1KyS2mqY4XxR37SuKcU8OjIK6iIhIA5GTE3kOaLlRdIic6svUxuwr9HP9sx/z7trvGZ1+HDkDT8blMtF/AJFqOjQ4ov5LnAZ1fdUVERFpACINjJdbGx32L6geKdWHhPQdvxYwbOoS5qz7nn+en8YtgzoqpEudUTyivjtOg7pG1EVEROqxihZqCSkv368ak0a/2b6HkU8uZfOOvTx8WQ8GdW4d/QcQOQgpiS56b/2UtOlzIfni+NqWFI2oi4iI1FsVbWKUnR0mpBdvYhQupJfZ8WjN1p38fvKH/LQrn6dG9VFIlzrJLF7Mdc/MZuH0FHwZtzjfXOOIRtRFRETqoYoWaim3PjpUPMu0zA0LN/zEmJkf0TQlgafHnsaJRzWNXsdFosg3YwOD/G9RQBJ3FhQwd8aLeOJoVF0j6iIiIvVM8cB4lUJ6ZfXoU6aUuuGVjzdzxZNLST2sES9fq5AudVsu/SkgCT8JFJBILv1j3aVqUVAXERGpRyJNGjWmXOZ2Lj799PC1MWVmmVprmZz7OX+e9Qm927Xg+TEeWjdrVDMPIRIlGSPaYhLAhZ+kZBcZI9rGukvVotIXERGResLrDR/Swy6/WNH66GVmmRYUBfjHq6uZlfcNv+16NPde3IXkBHd0Oy9SAzweOPeWDXy6PIXpf2sfb3NJFdRFRETqg4qWXwxZqKXii6FcbcyOXwsYM/Mjlny5nT8OOIE/n3Will+UuHLCKflsO+RbPJ72se5KtSmoi4iIxLlI80DT052FWkqpqDYmZH10gM9/3M1V05ax9ed93PeHrvyue2p0Oy5SC5omJ7BrX2Gsu3FAVKMuIiISpyqaB5qY6IyklxKpNiY9HRYtKhXSF238id89vIhd+4p45ppTFdIlbjVNSWR3fhE23OzqOk5BXUREJA4Vh/RI80BDljx3FC8FU1Z2drmLn1nyNSOfWEqrZim8et3p9GrXIvoPIFJLmqYkELDwa4E/1l2pNpW+iIiIxKHx46EwzG/zw+42WlEBe0g9uj9guevNdTy+8EsyTjqChy7tXrIFu0i8Kv47vGtfIU2S4yv6akRdREQkjlQ0kh5xt9FwIb1Mbczu/CKumZHH4wu/5IrT2jF1RC+FdKkX2qxfwbW+5ylcuCjWXam2+PpaISIi0oB5vTB2LAQC5c9NmVJm+UWoeJZpyFIwm3fs4erpeWz4YTd3DDmFy/vG11rTIhH5fHhG/wG7rwczP/yAMx8+Ck9W51j3qsoU1EVEROKA1wtjxkTebbRcSM/JCR/Syyy/uPzrHWTNyCO/KMC0K3tzRocjottxkVjKzWVJfk/O4V0KipJIGmeZ25m4WU9dpS8iIiJ1XGUhvdRuoxC53KXMxa+t2MJQ72IaJyfwyrWnK6RL/ZORQa57AAUk4SeBAn8Cubmx7lTVaURdRESkDosU0sPuNgqRQ/qwYaVC+vQPN3Hr7DX0ad+CKcN7cljjpOh3XiTWPB563JuM60Y/FhdJyS4yMmLdqarTiLqIiEgdVbyiYtmQnpYGCxeGCemR1kkvM8t06oIvuHX2Gs5OO4qnruqjkC712hljutDysjwuuGoHc+fGT9kLKKiLiIjUSZEGxl0umDo1TNioaJ30kJH0R+d/zr/fWMegzq14eFgPkhPc0e24SB1zSJKbQ47dyekX/hRXIR1U+iIiIlLnRBoYLy53CRvSq7BO+v/mbeDedz/jt12P5r5LupLg1nid1H/GGJokJ7BrX5iNB+o4/QsVERGpQ4pr0stKT69muUvIOunWWu6b8xn3vvsZv+t+jEK6NDhNUxLYta8o1t2oNv0rFRERqSMiTRxNT4f586tR7hJyg7WWe99dzwNzN3BRz1TuvVghXRqepimJ/BKHQV2lLyIiInVApJBeZgPR/Soqd5k/H3BG0ie89SlTPviCS/scy51DOuNymeh3XqSOaxqnpS8K6iIiIjEWKaSnpUWYOFrFcpc7Xl/HE4u+ZHjfNtx+wSkK6dJgddu8lqNXLIUuxNWyL/rdl4iISAxVtE56tVZ3KVPuctvsNTyx6EuuOK0ddwxWSJcGzOfjpnuu4/g3VnN3+lv4vKti3aMq04i6iIhIjPh8MHZs5M2MqrW6S7DcJRCw/P211Tyz5GuuOaM9fx3UEWMU0qUBy81laWFPzmEOBUVJJI2zzO0cHwPrGlEXERGJAZ8Prr4aAoHSxyNuZhQppIeUuxT5A2S/tJJnlnzN2IzjFdJFADIyyHUNoIAk/CRQ4E8gNzfWnaoajaiLiIjUMp8P+veHwjJz2yKWuwwfDk8/Xb6h9HQnpHs87Cv088dnP2bO2u/581kncn3mCQrpIgAeD+4/7cF1vx9wkZTsIiMj1p2qGgV1ERGRWlQ8kl42pBtTQblLuJAesuPorn2FXD09jyVfbudfF3Ri5GntaqTvIvHq2ItPoOUPeYw6rg/nD3TFRdkLKKiLiIjUGq/XqUkvW+5iDDz6aDXKXUJC+k+787niyaV8+u0uHhjajcHdjqmZzovEsaYpCSQf8zNXXpfPCUcmxro7VaagLiIiUguKJ45WOaRHKncZNqwkpG/esYcRjy9l6869PDayF2eedGTNdF4kzh2a4oTzeFtLXZNJRUREalikiaMVjqRHKneZOROADd/v4qLJPn7anc/Mq05VSBepQNMUZ2x6V5ztTqoRdRERkRpUE+UuK775mSueXEqi28Ws0R46tj60ZjovUk80LRlRj6+grhF1ERGRGlLtcpdIIT2k3GXhhp+47LHFHJqSyItjFNJFqqJpSgI9tqwj9dH7nX+YcUJBXUREpAZUu9zF6408kh4sd3lr1beMmraMNi0O4cUxHtq2bFwznRepZ5qtyOPp5/5G5yn3QmZm3IR1BXUREZEo83qhXz9Yu7b08QpD+pgx5RsKKXd5Pu8brntmOZ1TmzEry8ORh6bUTOdF6qFGHy4g0V+EKxCAggLiZccj1aiLiIhEUbXLXYpDurWlj6enl4T0xxd+yR2vr+WMDocz5fKeHJKk//sWqQ7XmWfygasfiwL9GOBehCdOdjzSv3QREZEoqXa5S3GqLxvSExNhwgSstdz33gYenLuBc09pxf1Du5Gc4K7RZxCpj3x4OIv3sNbFnQbm4iYe9jxS6YuIiEgUVLvcJVKqT0uD+fMJnNqXf/3fWh6cu4FLeqXy0KXdFdJFDlBuLvgDCQRwU1DkjpfKl+gEdWPMQGPMemPMRmPM+DDnk40xs4Lnlxhj2oWcuyV4fL0x5pzK2jTGtA+2sTHYZlLw+I3GmLXGmJXGmLnGmLYh9/iNMSuCP7Oj8cwiIiLFiqtXqlXuEi7Vu1wwdSpFfU7l5hdXMu3DTVzVrz0TL+xCgltjayIHKiMD3AkBjMuSlOS8jwcH/a/eGOMGHgbOBdKAS40xaWUuuwrYYa09AbgPmBi8Nw0YCnQCBgKPGGPclbQ5Ebgv2NaOYNsAHwO9rLVdgBeB0Knze6213YI/FxzsM4uIiBSLVGJeaU16uFQ/eTL5vftw3TPLeWn5Zm78zYn8/byOGGNq9BlE6juPB865eT0nDtrE3LnO+3gQja/nfYCN1tovrLUFwHPA4DLXDAamB1+/CGQa5391BgPPWWvzrbVfAhuD7YVtM3jPgGAbBNscAmCtfd9auyd4fDGQGoVnExERieiAQ3qEG34dOYqrpuXxzprvufW3aVyf2UEhXSRKjj8ln6PSN8VNSIfoBPVjgG9C3m8OHgt7jbW2CNgJtKzg3kjHWwI/B9uI9FngjLK/FfI+xRiTZ4xZbIwZEulBjDFZwevyfvzxx0iXiYiIkJMDo0eXz9wuVzUnjgZv2Dn8SoY/voQPP/+Jey/uypWnt6/R/os0NE1TEuJuZ9J6t+qLMWY40AvoH3K4rbV2izHmOGCeMWaVtfbzsvdaa72AF6BXr1627HkRERGIvIFoWhpMnRrm1+oVTRydOpUfTunOCK+PL378lUeG9WTgKa1qrO8iDVXTlER2x1lQvpAxnwAAIABJREFUj8aI+hbg2JD3qcFjYa8xxiQAzYBtFdwb6fg2oHmwjXKfZYw5C/gbcIG1Nr/4uLV2S/DPL4BcoHv1H1NERCTyBqKJiRFCeiUTRzef3JVLHvXx1bY9PHFFb4V0kRpywueruHrhc+QvWBjrrlRZNIL6MqBDcDWWJJzJoWVXVpkNjAy+vgiYZ621weNDg6vCtAc6AEsjtRm85/1gGwTbfA3AGNMdmIIT0n8o/mBjzGHGmOTg68OB04Ey/2spIiJSueJyl7LS02H+/Agj6ZF2P5o8mY3Hd+biR31s/7WAmVefSr8Oh9dY30UaNJ+PITeN4MYFM0k652zn32YcOOjSF2ttkTFmHPAO4AaesNauMcbcDuRZa2cDjwNPGWM2AttxgjfB657HCc5FwHXWWj9AuDaDH5kDPGeM+TfOSi+PB4/fAzQBXghOvPk6uMJLR2CKMSaA88VkgrVWQV1ERKolUrlLcUgvp5Ldj1af9wdGTPHhMoZZoz10bH1ojfRbRIDcXFyFhbhsAFtQ4CysHgezSo0tO6lFAKdG/f/Zu/fwpsv7/+PPO2lSQAWkIh5QFA/TOhAQgXAoURRPm+Cmzg0GisjJOZkbLe6obk4O8zA3BSoeQHCocyr6VflpNSAQhCooiiIgnlBBAfFEmzS5f38kadM2aYttyaGvx3X1sr3z+XxyZxN9efd9v+/S0tJUT0NERNJAcXHilXSXK8lKenFx8pX02bNZffbFXPnAGtq2drFgbF+OPeSAZpu7iAB+P6Ezz2RlWS9ezhnCGXddgmdct1TPCgBjzKvW2t6JXtPpCSIiInWIdVSs6XuVu8yezUsFw/nlva/QsW0uj07wKKSL7A8eDw//6SWGOF7kT+EbGDK5W0ZUvyioi4iIJJGs7XmdIb2Ocpen+v6Iq+aXckKnA3l0vIcj2rdu1vmLSJU13/QgaF2Eww5i1S/pTkFdREQkgWQh3eWCadOS3JCou0s0pC/ofg6/XrSWXkcfzENX9SPvwNxmm7uI1DZ4MBinxeG0uN3g9aZ6RvVTUBcREakhWUjPz6+jJn3ChIQr6aFZs7n5yIH88Yk38Z7YkXlj+tC2latZ5y8itZ0xyEmny1Yx7MrdlJRkxF7S7DvwSEREpDGShfRo2/PkIb3mDcZQ/u+7uLpVL154eSujPV3404/yyXFqjUwkFQ5slUPukV8yaMgXeDwdUj2dBlFQFxERiaorpM+atW8h/cvb/8Uvgj/knXe2c8OP87l8wLHNOncRqZvTYei/4126P/gCHHhpRiypK6iLiIhQd7lLwpX0ZI3VHQ4+vuU2fvrtD/im7FvuHX06Z5x0aLPNW0QayO/nvgXX4woF4b9zyIT6F/3+TUREWrx9LndJFtLz81k1/0nO/voHOI3hvxP7K6SLpAufD1dFkNXhPtxS9hv88zelekb10oq6iIi0aHUsjCcvd0lwg3W5WHz1jUx+09C980HcM+o0Dj2oVfNMWkT2ndfLcsdAzgs9S8C6cd9vKBmV3ovqWlEXEZEWq46FcZYvh3HjEtyQ4IjS0KACZt94L9d+2Jrzf3g4D4/rp5Aukm48HqZ7b6OMXELkEKhwpn0vda2oi4hIi5RkYRyXK0m5y8iRsHBhreuDAwZy+ajprNi8k6vPOI7fnv0DHA7TPJMWkUY5fOjBsBSclozopa4VdRERaXGSLIwnP3G0qChhSA+7XFzX8zJWb93FPy45lSnnnKSQLpLGepwWotNlq/jLjeFM2EuqFXUREWlZkpW7xEJ6Q2/4qo+Ha7pfyusdT+DBkafRr2te009WRJpU3oG55B75JWN/Vc7h7Vqnejr1UlAXEZEWo65yl2nTEtyQpNzlg/Mu4uyeV3Hkwa15/PLTOfaQA5p+siLS5PIOdNNr29uYaavhwnPTfkldpS8iItIi7FO5i98PgwfXCukWeOXSsQzufiW9urTn8Un9FdJFMsjRG19n4aI/0GnGzTBkSOTPehrTirqIiGS9JAvjictdYiE9GKw2bIHnfzSacccO55LTOnPzRd1w52i9SySTdHptFaUVvVlOAd7yl/H4fGm9qq6gLiIiWS3JPtDk5S5TpyYM6b7ThzLulEsoOvckJgzuijHaNCqSadYdPowfMYUAbtzhACV5W0jfmK7SFxERyWL1bRxNWO6ybFm1ay3wYMHPmHjOZGaN6MVE73EK6SIZatWeU9lLq0gfdUdrfDu7pXpKddKKuoiIZKVk5S4jRsCCBTUG6yh3ueGCX/OM58c8Mro33Tu3b7b5ikjzO+MMg8MVxoYs7lyjPuoiIiL7U5J9oAAUFiYJ6WPHJgzps/v+lNVnX8yTVw9QSBfJAh4P9L16Pb0u2qY+6iIiIvtTkoVxIBLSp0+vMVhcDBMnQjhcOWSpCumlE4p49Oc9OTBX/7oUyRbntC7l2EApHkZDWleoK6iLiEiWSLIwDkTKXRKG9AkTwNrKIQuEjeEPQyfR5upJ3HPByTh10qhI9vD7uf7WX+EIBuGF+aT7srpKX0REJOMVF8PAgbBhQ+3XEpa7JAnp1hj+dM7VnHLDFP7843yFdJFs4/ORUxEkx4YhEACfL9UzqpNW1EVEJKP5/bWqVwAwBmbPhnHjatyQbCUd+Ov513DObX9k8Ikdm3vaIpIKXi8hl5sV5b142QzhjLwfpXXxi4K6iIhkrFi5S2NDesgYbh0+mV/MupETOx3U7PMWkRTxeJh93XP8dno/wmEX7skOSrqlb/WLSl9ERCQjJSt3SRrSi4pg/PhaIX1T3lH88bpZXDn/FoV0kRZg3d5eBK2LUNiR9tUvCuoiIpJx9rncJcHJRxYIYXjqmpu44ZaxHHJgbrPOWUTSQ8FgME6Lw2lxu0nrXuoqfRERkYzyvcpd4kJ6bD09ZAwv/vpGrvvzaJ00KtKCnFngpNNlqzij7SlcM7J92pa9gIK6iIhkkGR90hu6kh4L6auP+iFlN/2NoZcPa9b5ikj6yTvQjQc/1wYfpy8/JZ17qSuoi4hIxpg6dR9C+siRCY8nLe3SDYfPx+BjOjTfREUkbbV5dQ0LF/2R3HAQFtyd1r3UVaMuIiIZoagIli2rPpYwpMeW3eNCeuy00Qqnk86z7+B0hXSRlsvnwxUK4ginfy91raiLiEjaS7AXFKgjpMctu8fKXd4+oQdHF9/J4d5BzTpXEUlzXi8rHANZHh7Imc4VeNJ4N6mCuoiIpLUkFSwUFiYI6WPHJgzpawsuoNuLi3E59YtkkZbOj4ezTQkV1nCzgRKcaVulrn9iiYhI2ioqSh7Sp0+PG0jQVD1W7vL6L8bR0/eUQrqIAJFKl4qwE4uTQIUznStfFNRFRCQ9JSt3SRjSJ0yo1q/RAmEMb/9lJj0WzlH7RRGp5PWCyxnCEMKdE0rrPuoK6iIiknaShfQRI2qE9CSnjYaN4dPpt3PKDb9r9rmKSGbx4OcFzuKv/IkX7BA8+FM9paRUoy4iImmlwSvpSU4btcA3t/+Lztde3ZzTFJFM5fMxILScQSzFhpyRWhi1ZxQREanbPpW71DjIKLKS7qDirlm0U0gXkWS8XqzbRYVxYF1u0rn2RUFdRETSQo3sXSnhSvr48ZU/xopePju1D47lL+OeNKFZ5ykiGc7j4f2HF3PboJGsmftI2q6mg0pfREQkDTR4JT1Jr8Y9p3s4fPXK5pugiGSV1gUDuf3RNry24hSu7Zq+WV0r6iIiklLJQnpBQVxIjzttNLaCHit3weWi/T9v3S9zFZHs8O56N9sX9eOROe0YMiTyj5h0pKAuIiIpk6zcxeWCadPiLho4EJYtwwKGqnKX8KBBmKVL03c5TETSkn+5Exsy2LAhUG7Ttpe6grqIiKRErP15TQUFUJm9a/RIjw/pjBiBc9kyhXQR2WfevPW0tmU4CeIO78Wbtz7VU0pINeoiIrLfxfJ3XPtzoCqkJ7so9p2pVbwuItJwnp1P8/94moWMBGNgbRugW6qnVYuCuoiI7FfJQnqtcpcJE7DWEjtT1AIYg5k9G8aN238TFpHs4/Vic5Ywr2I0Aetm3r1QMir9fkGn0hcREdlvkoX0/Py4cpdo+8VYSK/cNOpwKKSLSNPweHhk4I2U4yZEDuVB8M3/INWzqkUr6iIisl8kC+kOB8ydGw3p0faLsU2jlU4+GXPvvem33CUiGatr668I4wQsYZzkffYW0CXV06pGK+oiItLs/H6YODFxSJ81CzxUb78Yv5JuXC6FdBFpcnuP7o6DEGBwEGLnYaekekq1KKiLiEiz8vth7NjKxi2V8vNh+XIYR6T9ok3QftFUawEjItJ0zuj1FbkEcFCBgzB5bStSPaVaFNRFRKTZxFqgb9hQfbyy3IXIUrsNhzHUCOkjRiiki0iz8ex8mtv5DU7ChHEw+daj0u7gIwV1ERFpFjVaoFcyJq7cZezYypAONdovLliwP6crIi2N18tOZ0dCOAiTQ3nIkXYbShXURUSkySXbOGoMzJ4dV+4St9RuAWMMZs4c9UgXkebn8XDIgJMSbChNH00S1I0x5xpjNhpjNhtjpiZ4PdcY83D09VeMMcfEvXZ9dHyjMeac+p5pjDk2+ozN0We6v+97iIhI06tr42hlSJ8woVa5i6lM8Wq/KCL7x84OJ1bfUMohqZ5SNY0O6sYYJ3AXcB6QD/zcGJNf47Irgd3W2uOB24Hp0XvzgcuAU4BzgbuNMc56njkduD36rN3RZ+/zezT2czeHlcVv8OczS/AXp+cxtiIi9WnIxlGb4CAjhXQRSQXvYe9U31C6691UT6maplhR7wNstta+Z60NAIuAYTWuGQbMi37/X2CIMcZExxdZa8uttVuBzdHnJXxm9J4zo88g+szh3/M90oq/eD1njT+ev780mCHjj1NYF5GMU+/G0fWRkI5CuoikCc+oE7jD+duqDaXLfpJWGawpgvqRwEdxP38cHUt4jbW2AtgD5NVxb7LxPODL6DNqvte+vkctxphxxphSY0zp559/XueHbmq+x3YSiJ6OVUYu8+/YtV/fX0SkMWLlLkk3jtYI6ZU90ivrYRTSRSQFPB52nnYO4eiG0gAufI/tTPWsKmkzaRxrbbG1tre1tnfHjh3363t7f5qHkwrAYnFw/9t90+q/6EREkklW7lK5UL6lCDt+fLWVdAOYynoYhXQRSR3vlcfhJoCTIG6CeH+al+opVWqKoL4NOCru587RsYTXGGNygHbAzjruTTa+E2gffUbN99rX90grnnHdGJP/ChAGDEFy0uq/6EREEvFHDxStWe5SLaTPmBEZi7+gsh5GPdJFJLU847pRMmcLfx26gpI5W/CM65bqKVVqiqC+Bjgh2o3FTWTj5uIa1ywGRke/vxh40Vpro+OXRTu2HAucAKxO9szoPS9Fn0H0mU9+z/dIOz17QuT/kmiLoI6mnjtERFJr6lQIBquPxYf0cFxIr2wC43BE62EU0kUkPXjGdeP6Jd60CukAOfVfUjdrbYUx5lfAEsAJ3GetfcsYcxNQaq1dDNwLPGiM2QzsIhK8iV73CLABqACuttaGABI9M/qWRcAiY8zfgLXRZ/N93iPd7Pzc4iBEmBwMIda+luoZiYgkV1QEy5ZVH4uF9CuXjcQuXFi9/SJAQQFMm6aQLiLSAMbWbHQrAPTu3duWlpbu1/f0F6/HO/4EAuQCkEs5LxU+h2f68HruFBHZv4qKILpYXs2cwi2MWXEFzhUvAzVCemGhDjISEanBGPOqtbZ3ote0mTSNxOrUTbROPYCL+TO3R4pARUTSxMiRiUN64YiPuer2k6uF9Mq/KqSLiOwzBfU0M+raDrgIUtn9xY7GP39TqqclIgJEVtIXLqw9XlgIv3/3UggGK8tdKo0YoZAuIvI9KKinGc+4bowp2FK5ql6BEx+DUz0tEZGk5S6FIz5m8pI+tF2T4Ld/hYWwYEHzT05EJAspqKehUSPCuAhiCOEkhLfnV6mekoi0cMlC+i9O38jfH+rCYa+vAWqspM+Zo5V0EZFGUFBPR2vXVq/tXLs2hZMRkZYuWUifPORVHlxzEg4brl3uUliog4xERBpJQT0N+RhMBTlYnCp9EZGUShbSJw5ewz9KTq8d0EEbR0VEmoiCehryjupCjttgCOFyO/CO6pLqKYlIC1RcnDikX9F/Ff9a2hcHtnpIN0blLiIiTUhBPQ15PHDfo2W0K9jEzffu1rkgIrLfFRfDhAk1Ry2XnLace1Z6aod0hyN6HKnKXUREmoqCepo66wwn7Txb6HjcN6meioi0MLGQXv08PMspB7/NolcH4aBGuUt+PixfrpAuItLEFNTTVIc2bnp98jbH3PMvHXgkIvtNspCeQ5Di3VfWrkl3OGDuXPSrPxGRpqegnqZyVr/CQ//5I/3n3QFDhiisi0izSxbST2YDSxmMh1W1Q/qsWQrpIiLNREE9Xfl8lFacxoxwIf7yXuDzpXpGIpLF/H6YNKl2SDeEmMtY+tcM6QUFKncREWlmOamegCTmz/sRQ7mWAG7c4QAleVvQmpWINJepUyEUiv0USeuGELOZiIdV1S9W+0URkf1CK+ppyrezG2WmFSFyCDha49vZLdVTEpEsVVQEy5ZVHzuZt1jOIK5irk4bFRFJEQX1NOX1gtMFmDDuXIPXm+IJiUhWqn2gkcUQZi5X1S530WmjIiL7lYJ6mvJ4YPJtn9B+0Lv833MV2qslIk0uUUgHmMIM+qvcRUQk5VSjnsb69LVsWfU0PZ//FlxD1VlBRJpM7VNHLWApZDrT+X3VsDE6yEhEJEUU1NNY181vsHDRH2gVDsGt06GkRGFdRBqt9qmjkZV0hXQRkfSi0pc0duTrqymt6M208BS1aBSRJlHVhrFaH0YGsVQhXUQkzWhFPY2tO2wYP6FILRpFpEn4/TB2LIRClsj5opGw7iLAdK6vulAhXUQkLWhFPY2VftODvahFo4g0nt8PgwfDhg3VV9JP5i2W4q3qle5wKKSLiKQJBfU0NuRMBybHYhxWLRpFpFGmToVgMLaSHllNd1LBvVxVFdLz83XaqIhIGlHpSxrzeOAnw5+g9TsuJl1zLB6PVtRFZN9FDjSKX0mP9Eq/m0nVV9LnztWGdRGRNKKgns78fhY+OYKciiDOybnQTV1fRGTfFBfDjJmxkB6rS7fMZgLjmBsdNjBrlv75IiKSZlT6ks58PnJCQZw2DIGAur6IyD6JtGG00T2jVZtHC5lePaSrJl1EJC1pRT2deb2sdAxiWbg/ZzpX4lGRuog00MqVlgkTLNZW1aTXOtDI4YispCuki4ikJQX1NObHw1nmBYIYbjZQglPtGUWkXstXWC66rBxrc4mE9IjhPFEV0vPzVZMuIpLmVPqSxnw+qAg5sTgJVDhU+SIi9Xp5eZjBgy1ffJwbN2pxEKKQmZEftXFURCQjKKinMa8XXG7AhMlxofaMIlKnsmCIkdd+STgUK3eJlLwYwsxiYqTDizaOiohkDAX1NObxwJz/fEP+qct58MI5ePCnekoikqa+Lgsy8Ir3+fC19nGjkZBe2eFFG0dFRDKKatTT3HmtS7n0zXPJfSMET7mhRC0aRaS6Xd8GOH3Ydt4r6RodqVpJV0gXEclcWlFPcwevWYkrVIEjHFKLRhGp5bM9ZZxSsJP3SjpHR6o2jw7jSYV0EZEMpqCe5lxDziTodBFyOMHtVqG6iFT6cOd39B72GZ+9dlh0pKoNo4tAZPOoQrqISMZSUE93Hg8jhy3g8lP+h/+OV1T2IiIAbPzsa/petJ1tS7tER6oONCpgKUvx4jGvKKSLiGQw1ainOb8fnlx8EaEKB49NNpR0U1YXaenmPfE1kyYH+e6DY6IjVSF9BA+ygNFaSRcRyQJaUU9zPh+EKwxYQ6DcqkRdpIUruvlrLr/oAL774ODoSPypo9MU0kVEsoiCeprz5q2nlS3DSRB3eC/evPWpnpKIpMidD+1ixp8OoKpHetVKeiHTq04dVUgXEckKKn1Jc56dT/O8eZpltoDBjpfx7LwA6JbqaYnIfjZ93uf86be5YGMBHapW0uNCemGhQrqISJZQUE93Xi993TfRN7AKZ24ueGemekYisp+NmfoF90/PI771Yq0+6RAJ6dOnp2KKIiLSDFT6ku48Hlbd8zC3DRrJR48u1k5SkRbmF7/dwf3TO1Cz3EUhXUQk+2lFPQO84R7ELZzIIeVHMCbVkxGR/eZnv/mMR+44lKqADgrpIiIth4J6mvP74brL21Ne3o5Jqw0nv6hFdZGW4Oe/3aGQLiLSwqn0Jc35fBAMAtZBMIjaM4q0ACN/t4NFtx1C9c4uFgeh6iG9oEAhXUQkiymopzmvF9xuwITJybF4vSmekIg0q1FTPmfhrfEhPSKft1jOoKqQ7nLBtGkpmaOIiOwfCuppzuOBJUss7Qe9y69mblPZi0gWu6LoCx78R6y7S1W5i4MQc7kKD6siQ/n5sHSp6uBERLKcgnoGKBjk4Nyuz/Kj0tsiResiknVumPspD8yM7+5SVe4yi4lVId3hgLlzFdJFRFoAbSbNBH4/9y+8HlcoCI/OgZIS/UtaJIv89d7PuHnqATUOM4qUu1RbSXc4YNYs/fkXEWkhtKKeCXw+XBVBnOEwBALaUSqSRcb/YRd/HnsoFTsPihtNUu6yfLlOHRURaUEU1DOB10tFjouQwxHZWaodpSJZYdKfdlP89/YkOsxI5S4iIqKgngk8HmZOuYsF549V2YtIlvjVn3cz62/tqLdPujEqdxERaaFUo54h1ucO5N4KLydyHPrXtUhmu33BF9z1t/iNoxC/cbRaSJ89W+UuIiItVKNW1I0xHYwxzxtjNkX/enCS60ZHr9lkjBkdN36aMWa9MWazMeZOY4yp67km4s7o9W8YY3pFx3sYY/zGmLei4z+Le48HjDFbjTHrol89GvOZU8Hvh//dfALvLzmWIUPU+EUkk/3zoV0UTXbX2Dhqa/dJB5gyRSFdRKQFa2zpy1SgxFp7AlAS/bkaY0wH4C9AX6AP8Je4QD8LuAo4Ifp1bj3PPS/u2nHR+wG+A0ZZa0+JPuMOY0z7uGlMsdb2iH6ta+Rn3u98PghVGLAOAgGrvaQiGerf/9nF5F+2I1jfxlHQqaMiItLooD4MmBf9fh4wPME15wDPW2t3WWt3A88D5xpjDgfaWmtXWWstMD/u/mTPHQbMtxGrgPbGmMOtte9aazcBWGs/AXYAHRv52dKG1ws5LgsmrL2kIhlq2bufM6XIQthBnRtHQaeOiogI0Pig3sla+2n0+8+ATgmuORL4KO7nj6NjR0a/rzle13OTPauSMaYP4Aa2xA3fHC2Jud0Yk5vswxhjxhljSo0xpZ9//nmyy/Y7jwf+eNcO2g96l0VPBLSnTCTD+LfsZPjoryj7qEPcaIKNo6BTR0VEpFK9Qd0Y84Ix5s0EX8Pir4uuitumnuC+PDe6Sv8gcIW1Nhwdvh44CTgd6AAU1fFexdba3tba3h07pteCfO8+ljOOfpp+vr+rSF0kg7y5bQ8/HvUlu1d1jY5U1aXXCulOp9owiohIpXq7vlhrz0r2mjFme7T05NNoSN6R4LJtgDfu586ALzreucb4tuj3yZ67DTgq0T3GmLbA/wF/iJbFxOYfW5kvN8bcD/yujo+btjq/s46Fi/5Aq3AI7pipNo0iGeC9z79hyM92sXNl7ZBeyPTqId0YuPtu/bkWEZFKjS19WQzEuriMBp5McM0SYKgx5uDoJtKhwJJogP7KGNMv2u1lVNz9yZ67GBgV7f7SD9gTDfNu4HEi9ev/jX/zaNAn+h7DgTcb+ZlT4tBXV+EKVWDCIZ1OKpIBPt2zl7PHfcSOFV2iI7GQHqaQ6Uzn91UXqw2jiIgk0NigPg042xizCTgr+jPGmN7GmLkA1tpdwF+BNdGvm6JjAJOAucBmIjXlz9b1XOAZ4L3o9fdE7we4FCgALk/QhnGhMWY9sB44BPhbIz9zSljvYILOHMJOp04nFUlzu78N4LloB+89cRI1e6UXMpPpXF/9hmHDFNJFRKQWEykBl5p69+5tS0tLUz2NSru/DXDlhDv5/QHb6T36J/r1uEia+ra8gh8M3MW20tg+F1P5WsFBa1n6da/qN7hc2jwqItKCGWNetdb2TvRaY1fUZT85qFUOrx15MisuHqd/oYukqUBFmD7DtycM6S5HBdO+nlT9BnV4ERGROiioZ4gcpwOzI4+nH2yrpi8iaSgUtvS7aDsbnjsiOhK3kn78JywNF1TvlW6MOryIiEidFNQzhN8PHy44nZIFhzJkiDo0iqSTlSstx/7wW9Y+fVh0pCqkj+izkaVbOuOhxh/aKVMU0kVEpE4K6hnC54NwhQMbNmr6IpJGioth4ED46O0DoiNVIb1wxMcsKM2HmnuBCgth+vT9N0kREclICuoZwusFZ04Y47Bq+iKSJoqLYfx4G83h8d1doln822sgHK5+0/DhCukiItIg9R54JOnB44GxI57hC38Fv73ueDyebqmekkiLVlQEM2bEVspNtdcKC2E6RfDEE9VvcjgiL4qIiDSAgnqm8Pu586FLcASD5EzOhW46mVQkVZKF9Mpzi7YUwYwZ1W8yBmbN0p9bERFpMJW+ZAqfj5yKIDk2rJNJRVKouLhmSI9873BEQ3o3P8ycWf0mnTwqIiLfg4J6pvB6CbncVBgHVkXqIilRVBSpSY+IhXRDQQEsXx4N6WPH1t48OmWKQrqIiOwzlb5kCo+HZ29/kHcWPcWkG8dyoH59LrJfjRwJCxfWLHeJhPSlS4n0TB08GILB6jcWFGjzqIiIfC9aUc8gZaf35W7PpezuflqqpyLSYsTyd/WQHvne5YJp06LDM2bUDulOZ9wFIiIi+0ZBPYNse6cNe/zHsWx5uP6LRaTRYiF92bLE5S5Ll0b3hhYl6PBiDNx9tzaPiojI96bSlwzh98P1V3WgvPxgrlxt6Pqi/v0v0tymToVgMBLMIyLfjxgBCxZEh4qSdHhwFg78AAAgAElEQVTR5lEREWkkrahnCJ8v+lt16yAYVNMXkeZUeyUdYiG9sDAupPvV4UVERJqPVtQzhNcLbjeUlYVwmRDevI2ADj0SaWrFxTBxIoTDtctd5syJy99+dXgREZHmpRX1DOHxwNMzX+dG8xdKQmfgmdw3EhREpMlE2i/GQrohPqQXFtYI6YMHw4YN1R+gDi8iItKEtKKeQc7Y8wyD7S3RQ4+ckfoXFaqLNIlI+8XYT/E16VBYWCN/q8OLiIjsB1pRzyCOM84g6HQRcjgjdTA69Eik0araL8aPWsDicMCcOaZ6SC8uVocXERHZL7Sinkk8Hq4dO5NhX77LBb/+hUKBSCMlPqMosoo+cJBlxnRH9T9mxcUwYUL1h2jzqIiINBMF9Qzz8Uk9eLx9Py7wnJ7qqYhkvEj7xfiRSEi/7Och/vNQjX88+v0waVLtzaPDhimki4hIs1DpS4Zp2zqHr/ZWpHoaIhmtqv1i/Gik3OXa6xKEdIik+lCo+pjLFSlgFxERaQYK6hmmbSsXX5UF679QRBIqLoaBAxOFdJh5RwV33JogpBcV1bwB8vPjjiYVERFpegrqGeabj9rx9rNHqDOjyPcQKzEPh+NHIyF9/DVBfnetO/FNiQ41mjtXIV1ERJqVgnoG8fvh0ZuO48PnuzJkiNqoi+yLWI/06iXmkXKXKyaUMfvOJCF9woTEhxoppIuISDNTUM8gPh+Eg4B1ECgP4/OleEIiGcDvh549I63Pq4uE9Otu/Ib7ZrVOfOPEibVDeq2m6iIiIs1DQT2DePPWk2vLcBLEHS7Dm7c+1VMSSWtFRdC/P6xbFz8aCegYy7jrv+TWP7dNfPOMGTVrZGD4cIV0ERHZb9SeMYN4dj7N8+b/WGYHMdjxMp6dFwDdUj0tkbTj90eatNTc/xnj7ryLXxV+x63XHJX4gqKi2ocaORzq8CIiIvuVgnom8Xrpl3sTfcv9ONy54J1Z/z0iLUxxcaRipeZieISlTf42rrv5S24adkriBxQV1a6TMQZmzVJduoiI7FcqfckkHg/vPPg4tw0ayZvzH1NoEKkhcVeXiEOPqKDDuesZUfQZN1x4CsaYxA9I1OFFJ4+KiEgKaEU9wzgG9Ofu0hDdTupB91RPRiSNJFoIjxk57jteOWQp3Tu3486f98XpSBDSk508OmWKQrqIiKSEgnqGadfaBaBDj0TijBwJCxfWHjcGbpixl0e/eZmj27Zh7ujetHI5Ez8k0cmj6vAiIiIppNKXDNM2GtT37FVQF/H7YfDgxCHd4YBptwV4JrCSNm4n88b0oX2bBL3SIfHJo+rwIiIiKaagnmEOcDtxGPhqb0WqpyKSUsXFMHBg4s4uBQXw3AsVPB/28215BQ9c0Ycj2yfolR57UKK6dHV4ERGRFFNQzzCrVhnavdAe1+3P6mhSabHq2jQ6YgQseSHEPRvX8MHO75gz6jROPjxJr3SdPCoiImlMQT2D+P0w5IwQ60v7cv2Scfi91yusS4tTVATjx9fO1hBZBJ833/Kbh9ex+v1d3HrpqfQ/7pDED9LJoyIikuYU1DOIzweBgCFEDgFc+IIDIoMiLcTIkYk7uxgDc+bAtGmWm556i2ff/Iw/XnAyPz71iOQP08mjIiKS5hTUM4jXC263xUkQN0G8rhWRQZEsV9+m0Vib89lL32Oe/wOuGnQsYwd1Tf5AnTwqIiIZQEE9g3g8UPKSk7PPfJ3Jnjvx+G5RHa1kvfo2jS5fHgnpC1Z9wPTn3uHCU4/g+vNOTv5AnTwqIiIZQn3UM4zHA90nuil5t7dChWS9ug4xGjECFiyIfH//iq3c+NQGzjzpUGZe0h1HogONILI0r5NHRUQkQ2hFPQO1bZ2j9oyS1fx+6NkzeUgvLKwK6cXLtnDjUxs455ROzB55Grk5SQ408vth7FidPCoiIhlDK+oZqG0rF3uDIQIVYdw5+m8tyS51raI7HJEKlViuvuulzcxcspELuh/OHT/rgcuZ5M9DrMg9WOOgsIICbR4VEZG0pZSXgdq1iZxO+nWZTieV7JKsqwtUr0e31nL78+8yc8lGhvc4gn/WFdIh8tCaId3phGnTmm7yIiIiTUxBPQO1beWi17a3MdNuUR91yQp1dXWBSD360qWRbRnWWmYu2cg/SzZx8WmdufXSHuTUFdKLi2t3eDEG7r5b+zxERCStKahnoF2L36fHQ1/yzi0vwJAhCuuS0erq6nLMMZH+6LF6dGstf3/mbe72beHnfY5mxk+740y2cTT28AkTqo9p86iIiGQI1ahnGL8frv7b6QTDfZlGgJLyoXh8Pq0MSkaK5ehEp4zGd3WBSEi/8akNPLDyfUZ7unDDhadgTB0h3e+HSZNqP3zYMIV0ERHJCFpRzzA+HwTDOVWnkzrO1KFHkpGKimD8+MQhPb6rC0A4bPnDE2/ywMr3GTvw2IaF9LFjIRSqPu5y6VAjERHJGFpRzzBeL7hyDaGyEDlOi/euS8DTLdXTEmmw2EL3unW1X0tUlRIKW6Y+9gaPvvoxE73HUXjOD+oP6Yk6vOTnw9y5+u2TiIhkDK2oZxiPB559Lkz7QZv49T934BmnkC6Zo6gI+vdPHNIdjtohvSIU5rePrOPRVz/m2iEn1B/SIXmHF4V0ERHJMArqGWjwIAeHDHyPvK7fpHoqIg0SW+RuSOvFmFDYct0jr/PEuk/43dAT+c3ZJ9Yf0v1+WLy4+pg6vIiISIZS6UsGMsbQtpWLr/aqj7qkv+JimDgRwuHEr9fcNAqRjaM3PfUWi1//hMJzf8Ak7/H1v1GsLj3+jdThRUREMpiCeoZq29rFV2UVqZ6GSJ3q6upyzDFw/fWJM/Tdvi3M83/AVYOObXhIT1SXrg4vIiKSwVT6kqGOeHU3O2/+EH/x+lRPRSShukJ6YSFs3Zo4Qz9S+lHliaPXn3dyw95s6tTEdenq8CIiIhmsUUHdGNPBGPO8MWZT9K8HJ7ludPSaTcaY0XHjpxlj1htjNhtj7jTRAtRkzzURd0avf8MY0yvuWSFjzLro1+K48WONMa9E73nYGONuzGdOB/7i9Ty+6BweeesChow/TmFd0k6ykG5M5ACj6dMT3/fiO9u5/n/rGXTCIcy4+FQcdR1mFFNUVPu0JNWli4hIFmjsivpUoMRaewJQEv25GmNMB+AvQF+gD/CXuEA/C7gKOCH6dW49zz0v7tpx0ftj9lpre0S/Lowbnw7cbq09HtgNXNm4j5x6vsd2EsBd1Uv9sZ2pnpJIpWQhPVFXl3ivfbibSQtfI//wtswaeRrunAb846moKPEOVdWli4hIFmhsUB8GzIt+Pw8YnuCac4DnrbW7rLW7geeBc40xhwNtrbWrrLUWmB93f7LnDgPm24hVQPvocxKKrtCfCfy3njlmFO9P83ATwEkQN0G8P81L9ZREgOSHGOXn1+7qEm/zjm8Y88AaOrVtxX2Xn86BuQ3YPjNyZOKQXliokC4iIlmhsUG9k7X20+j3nwGdElxzJPBR3M8fR8eOjH5fc7yu5yZ7FkArY0ypMWaVMSYWxvOAL621FQmur8UYMy76jNLPP/882WUp5xnXjd9ds5yhxz7BC3M2q5e6pIVkudnhqLuF+favyhh932pyHIb5Y/rQ8aDc+t+sqAgWLqw9XliYvK5GREQkw9S7bGWMeQE4LMFLf4j/wVprjTEJto01zj48t4u1dpsxpivwojFmPbBnH9+rGCgG6N27d5N/lqZ0+E+OY0Obd+hxeX6qpyItnN8f2ctZs0wcIiF91qzkIf2rsiCj71vNl98FWDTOQ5e8A+p/w2TlLgrpIiKSZeoN6tbas5K9ZozZbow53Fr7abQEZUeCy7YB3rifOwO+6HjnGuPbot8ne+424KhE91hrY399zxjjA3oCjxEpj8mJrqrHv0dGO7iNC4AvvwvSxq0um5IadfVILyiAadOSh/SyYIir5pWyecc33H/F6XTr3K7+N0wW0keMUEgXEZGs09jSl8VArIvLaODJBNcsAYYaYw6ObiIdCiyJlrZ8ZYzpF60lHxV3f7LnLgZGRbu/9AP2RMP8wcaYXABjzCHAAGBDtPb9JeDieuaYcdpHg/ru7wIpnom0VLFNo4lC+ogRsHRp8pAeOXV0Ha9s3cU/LjmVQSd0bNgbJltJr3likoiISBZobFCfBpxtjNkEnBX9GWNMb2PMXABr7S7gr8Ca6NdN0TGAScBcYDOwBXi2rucCzwDvRa+/J3o/wMlAqTHmdSLBfJq1dkP0tSLgOmPMZiI16/c28jOnhfZt3PTa9jYH3jYzUnsgsh8l2zQK9edmay03PvUWz6z/jD9ecDLDeybdNlL7DRO9mVbSRUQkSxmb6N+0Qu/evW1paWmqp5HU//6xhjeLnmSI9TGg1WtQUqKe0dLs6qpHN6ZhXRHvLNnEbc+/y1WDjuUPFzRgj0WycpeCgsiyvYiISAYzxrxqre2d6DUVN2cgvx9GXt+DQLgn0whQUj4Uj8+noC7Nqq569Nim0bpCejhs+dv/vc19K7byk55HNuzU0WQh3eWKFMCLiIhkscaWvkgK+HwQCOdUHXjkOBO83lRPS7JYXfXoBQV190iHyMbRaxat5b4VW7liwDH845IGnDpa30q6/sNURESynFbUM5DXC+5cQ1lZmByHxXvXJeBRL3VpHsnyMjSsRHzP3iDj5pfyytZd/P78k7hqUFci+8frUNfGUdWki4hIC6GgnoE8nkhJ+s9ueI+BBVYHHkmzGTky8blCDa1H/3TPXi6/bw3vffEN/7ysB8N6NGDjaGz5viaFdBERaWEU1DOUxwOnnP8pbdq3TvVUJAs15BCj+kL6u9u/ZvR9q/m6rIIHrujDgOMPqf+NYyG95ib3ggKFdBERaXEU1DNY+9YuvlQfdWlijTnEKGb11l2MnbeGVi4nD4/vxylHNOAwI78/8sY1Q7o2joqISAuloJ7Ben3yNh3W+KGH0cY6aRLJFrQhcohRQ84Vemb9p0x+eB1HHdyaeWP60PngNg178xkzav/XQX4+zJ2rv79FRKRFUteXTOX30/fmeXz2zCH4vdfr0CNptMYcYhTzoP99rn7oNbod2Y7HJvZveEgvKoInnqg+5nAopIuISIumFfUM5Z+/iXMrniGAm5sDAUrm/xePAo18T43dNGqt5Y4XNvHPkk2cdXIn/v2LnrRyORv25onayhgTKYTX39MiItKCaUU9Q/kYTAB3VS91Bqd6SpKB/H4YPDhxSHc4GhbSQ2HLn598i3+WbOKS0zoze2Svhod0vx9mzqw+1tD/OhAREclyCuoZyjuqCzkuMIRw5TrwjuqS6ilJhikuhoEDE3d2acghRgCBijDXLlrLg6s+YHxBV2Zc3J0cZwP/seL3w9ixtWttpkxRSBcREUFBPWN5PDDt/j20K9jEPQ/vVYWA7JNYPXqizi4jRjTs4M9vyyu4ct4ann7jU64/7ySuP//k+g8yiokt5W/YUH1cbRhFREQqqUY9gw0YYGi3fgtd8vOAA1M9HckQyerRoeFnCu36NsAVD6zhzW17mHFxdy7tfdS+TWLGDAgGq485nWrDKCIiEkdBPYO1b+0CYPd3wXquFImUuvz97/DBB7Vfa+ghRgCffLmXX977Ch/t3svskadxdn6nfZ9IzQ4vxsDdd2vzqIiISBwF9QzWvo0bgD069EjqUdcqekMPMQLYvOMbRt37Cl+XVfDgmD707Zq3bxNJ1uFFm0dFRERqUY16Bnt7nYsDlhzE3r8vVh91Saiuri7Q8Hp0gHUffckls1cSCFkWje/XNCEdYNgwhXQREZEEFNQzlN8P555t2biuP5OfHqtDj6SWurq6GNPwQ4wAXt70Ob+4ZxUHtsrhsYkeTjmi3b5NJllId7kiExEREZFaFNQzlM8HgYCp6qMeHBAZFCES0idMSNzV5dRTYcWKhjdXeer1TxjzwBqO7tCGxyb0p0veAfs2mZEjE4f0goKGL+eLiIi0QArqGcrrBbfb4iSImyBe14rIoLR4sdaLNduTQ2Txet26hmXjUNgy47l3uOY/a+lxVHseHu/h0LatGj6RuupuCgsV0kVEROqhzaQZyuOBkpec/PH61xgQehHPjFsUeiTpptF93a+569sA1y5ay8ubvuCy04/ihgtPafhpoxBZ0p84MfGSfkN7QIqIiLRwCuoZzOOBk8c5Wf5JX4X0Fs7vh6lTE9ej70vrRYA3t+1h/IOv8vnX5Uz7STcu63P0vk0mVneTaEl/xAiFdBERkQZS6UuGa9/GxW61Z2zR6to0WlAAy5c3PKQ/UvoRP5m1Emstj07wNG1I35fdqyIiIqIV9Ux3cBs3e/YGCYctDkcDj2+XrFHf4nVDc3F5RYgbn9rAQ698SP/j8vjXz3uSd2Bu00xGfdJFRES+FwX1DNeutYseH79N4K9raTV0iEpgWpD6Fq8bWmHy6Z69TFzwGus++pLxg7syZegPyHHu4y/bkk1mX+tuREREpJKCeoYre+5jejz0Ja/ho//0v0NJicJ6C9BUudi/ZSe/eug1yoIhZo3oxXndDt/3ySTrkZ6fD3Pn6u9HERGR70lBPYP5/TBlRj+C4f5MI0BJ+VA8Pp+CUZZrilz8yZd7mbfyfeYu38oxeW2Y88t+HH/oQfs+mWRtZhwOhXQREZFGUlDPYD4fBMI5hHEQwOJznIlHvdSzWrKQ3pBcbK1lzfu7eWDlVpa8tR1rLcN6HMlNw07hoFaufZtIQ9rMKKSLiIg0ioJ6BoscemQoKw/hclq8d10Cnm6pnpY0k+Li5CG9rlxcFgyxeN0n3L/yfd7+9CvatXYxduCxjOzXhaM6tNn3icQOMgoGa79WUADTpimki4iINAEF9Qzm8cCTz1Twsxu2MPHnbfGMU0jPVslW0uvKxdu/KuOBle+zaPWH7P4uyEmHHcQtP+nG8B5H0tq9D4cXxfP7YezYxCF9X9rMiIiISL0U1DPc2d4czj7mGYau+ghOvVQrmVmorpC+dGnt8T17g8xZuoX7VmwlUBHm7PxOXN7/WPp17YAxjWjhqdNGRURE9isF9QxnVq1i/n9+jysUhEfmqOtLlklW7uJyRVbS45UFQzzo/4C7fJv58rsgw3ocwXVnn0iXvAOaZiLqkS4iIrJfKahnOp8PV0UQpw1DIBDZYaqgnhUaWu4SClseX7uN2/7fRj7ZU0bBiR0pPOcH/PDIds07EYV0ERGRZqWgnum8XlY4B/JyxQDOdK5U15cskazrYXy5i7WWF9/ZwYznNrJx+9d079yOf1xyKv2PP6T5J6KQLiIi0uwU1DOcHw9nmRIqMNxsoAQnWk/PbEVFibNxrNwlHLasem8nd7ywidXv7+KYvDbc9YtenN/tsMbVoMdrSPtFhXQREZFmpaCe4Xw+qAg5sRgCgRC++R/g8XRJ9bTke0pcZWLp4wlx/pgvWLj1E371wk52fhvg0INy+evwH3LZ6UfhcjqabhJ1bRpV+0UREZH9RkE9w3m94M4JUxEI47ZBvPeNhlG3KEhlmMQL2JGNm4f0+JTtBWu5fzN0apvL4BM7MuD4Q7ig++G0cn3PNovJJNs0Cmq/KCIisp8pqGc4jwceufg+1j+0lTN5CU9ojTaUZpjq5wdZwBAL6R0HbOXiibvxHPdD+h+XR9dDDmi68paakm0aBbVfFBERSQEF9Sww8IoTOPuRX5EbDoHbHVlml4wxdWr8+UFVIX3UhDLuv+tYHI6uzT8JbRoVERFJO01Y2Cqp8k6bwfTt/gJ3nfMP9VHPILGV9GXLLLFwHvvrlEKYN6s1DkczrZ7XnESikO5wKKSLiIikkFbUM5zfD2edZSgrG8DvHH3pPXyjcnoGqNqvGQvoVSvpc+aY5s/Gfn+kzOXJJxPXo2vTqIiISMopqGc4nw8C5RZrHYRCBt/Vj+Lp9o0CVhrz+2HiRBttqhJbMY+E5cLCZgzpfj/Mnw+rVsG6dcmv06ZRERGRtKCgnuG8XnA7KwiEwU2QweEXwddaQT0N7fymnH8/vJvb/tSWcLg11UO6aZ79mg0N5zHaNCoiIpI2FNQznMcDJf9+hxcnPUJB6EU8uWvBOzPV05KoHV+XseSt7Ty7/lN8L4f4dGE/CFffGmKMafpS8LoOLEpEhxiJiIikHQX1LOAZ1403t+3h7/O7M2bC0Vzi6ZvqKbV4e/YGKfrvGyzZ8BnWwnEdD6DVutOjIb1qg2iTN1XZ14AOMHx4ZCVdv4URERFJKwrqWcDvh1/PGEBZueXFv0DnAmWuVNry+TdcNa+Uj3Z/xyTvcRxdcRR3zziAreurX9ekIT22OfSJJxp2/THHQI8eCugiIiJpTEE9C/h80T7c1kEoEMI3/wM8ni6pnlaLtOzdz7n6oddwOR0sHNuP0Gcd4g4zqq5JQnp93VviKZyLiIhkFAX1LOD1gjsnTCAUxm2DeO8bDaNuURjbj6y13LfifW7+vw2c2Okg7hnVm6M6tGHw5YlDemFhE4T0qh6Pya857DDo10/hXEREJAPpwKMs4PHAHect4Uxe5A6uxRNaHllml/2ivCJE0WNv8NenN3B2ficem9ifjze2iR5mVPv6RjdWKS6G/HwYPz55SDcm8kaffgqPP66QLiIikoG0op4F/H6Y/Ow5BLAsZxDdnJvweL2pnlaL8NmeMiYufJW1H37Jr888nslnncgrr5ik5S5z5nyPlfTiYrjjDti7Fyoq4OOPk19rDAwbphV0ERGRLKCgngV8PghUOAkBZRheumKeatSbWTAU5uE1H3H78+9SFgwxa0Qvzut2OH4/jB3bBOUusXC+fTvs2tWwe9S9RUREJKsoqGcBrxfcbigvtxgTpk+gBPwnK7A1g3DY8tQbn3Db8+/ywc7vOK3LwUz7STdO6HRQnSXjDS53+T7tFdUDXUREJCs1qkbdGNPBGPO8MWZT9K8HJ7ludPSaTcaY0XHjpxlj1htjNhtj7jTGmLqeayLujF7/hjGmV3T8DGPMurivMmPM8OhrDxhjtsa91qMxnzkdeTxQUgKTLn2XFxjCkHnjYMiQSOiTJmGtpeTt7Zx/58tcu2gdrV1O7ru8N/+d4KkM6RMm1A7pxkTKXRoU0ouLYeDAhof044+PvOny5QrpIiIiWaixK+pTgRJr7TRjzNToz0XxFxhjOgB/AXoTOSv9VWPMYmvtbmAWcBXwCvAMcC7wbB3PPQ84IfrVN3p/X2vtS0CPuPfbDPy/uGlMsdb+t5GfNa15PPDVnJdZERpILkE8gTWRmhitqjfax7u/4zcPr2PN+7vpkteGf17Wgx93PwKHI3JwUSyk1+yOuE990ouKIm0W63LYYZEvtxuuvFLhXEREJMs1NqgPA7zR7+cBPmoEdeAc4Hlr7S4AY8zzwLnGGB/Q1lq7Kjo+HxhOJKgne+4wYL611gKrjDHtjTGHW2s/jXu/i4FnrbXfNfKzZRS/Hy5adAUBLDcToMR5vjaUNoF3t3/NqHtX822ggpsv+iGX9j4Kl7PqF1FNEtJHjoSFCxO/pvaKIiIiLVZj2zN2igvJnwGdElxzJPBR3M8fR8eOjH5fc7yu5yZ7VrzLgP/UGLs5WipzuzEmN9mHMcaMM8aUGmNKP//882SXpaWqDaU5BHDjO3+Ggl0jvfbhbi6Z7SdsLY+M9zCib5dqId3vj9Sk1wzpDkcDQ7rfD4MHJw7pDkekZkbtFUVERFqseoO6MeYFY8ybCb6GxV8XXeWu52jEfbcvzzXGHA50A5bEDV8PnAScDnSg9op//HsVW2t7W2t7d+zY8ftPOgUihx6FcBLETQDvM4WqUW8E38YdjLjnFdq3cfHYxP6cfHjbaq/HurvUrEnPz29gyXhREQwYkLgevaBAdeciIiJSf+mLtfasZK8ZY7bHSk+iIXlHgsu2UVXGAtCZSCnLtuj38ePbot8ne+424Kgk9wBcCjxura1sjhe3Ml9ujLkf+F2yz5PJPB4ouWIhD8z+NvJfXxUVqlH/np5ct43fPvI6J3Y6iHlj+tDxoOq/hIkthNdswehwwNy5cf+T+/2RuvO1ayO1MO3bw+7d8PXXyVsujhgBCxY0/YcSERGRjNPY0pfFQKyLy2jgyQTXLAGGGmMOjnZvGQosiQbor4wx/aLdXkbF3Z/suYuBUdHuL/2APTXq039OjbKXaNAn+h7DgTe/96dNdz178iCjuYerGBL+f/jzfpTqGWWc+1ds5dpF6zity8EsGt+vVkiHSPfEmiHdmEiHxMqQXlQE/fvDE0/ABx/A++/DunWR75OF9MJChXQRERGp1NjNpNOAR4wxVwIfEFnRxhjTG5hgrR1rrd1ljPkrsCZ6z02xjaXAJOABoDWRTaTP1vVcIp1hzifS1eU74IrYRIwxxxBZbV9aY44LjTEdAQOsAyY08jOnLd/ObpSbMGHrIOBw4NvZDa2nN0x5RYjbnn+XOUvfY2h+J+78eU9auZy1risqql2tUmvjaEM6uNT5ABEREREwtuZOOAGgd+/etrS0NNXT2Cd+P5xxpqW8PEwrZ4gX79qIZ1y3VE8r7b20cQc3Ln6L93d+xy/6Hs1NF55CjrP2L5uS5e85cxoR0gsKYNo0lSiJiIi0UMaYV621vRO9ppNJs4jHA49PW8ua6/7HkNBLeCavhW4lCoFJfLjzO256egMvvL2drh0PYP6YPhScmHgTcbL8XVgYF9KTtVns0AHatq2qUTcGevRQy0URERGpk4J6lmn79nqc4TAOwhAIaENpAuUVIe56aQuzl24hx2GYet5JjBlwLO6cxFs2/H6YObP2eGFh9MRRvz9SuJ6og0vlRSIiIiL7RkE9i/j9cPYDI3XoUR1CYcuv/7OWJW9t58JTj+D355/MYe1aJb0+1oaxZoVYZf4uLo40U6/Zp7HaRSIiIiL7TkE9i1QdegTlgK/HZG0mjWOt5YbFb7Hkre38+Uf5jBl4bJ3X+/0weFCYYMhERwxgKWhTyvSHL4H55fDZZ+Gq+oEAABRVSURBVIlvHjFCIV1EREQapbHtGSWNeL3gdoPDESaXAINL/wFDhujgo6hZS7fw4KoPGF/QtUEhfexPd0VDeuzL4qSCad/9OtJmMVlIV5tFERERaQIK6lnE44GSErj0lNX8kvmYcKiqTr2F+99rHzPjuY0M63EEReeeVOe1xcUwsH+IDZ8eHDdqMYS5m0l4WJX4RmMiLWC0ki4iIiJNQKUvWejJd08nQG8eZBQl5twWX6e+cssXFD32Bp6uecy8+FQcDpP0Wr8fJo4PEcZBZBUdYiF9NhMYx9zENzockROP1AtdREREmoiCepbx+SAQdBDCEMDis4NbdJ36pu1fM/7BVzkm7wBm//K0pJ1dKC7G//eXGPvRnwlzEglD+mFPQ6tjqrdZPPpoyM+HUaPUXUdERESalIJ6lvF6we2sIBAGN0EGh0pg/hctLkRWhMK8+clX/Oqh18jNcXLf5afTrrUr8mJxMdx7b6QsaPdu+Ppr/LtOZDA+grjjnhIN6Sfcxrh5Y8BzT0o+i4iIiLRMCupZxuOBkn+/w7yJK6MtAy3cf3/Wr/haa1n30Zesem8Xr2zdSen7u/mmvII2bieLxvXjqA5tIhcmOZRoBlOiIb3GSnqf+xn3ypT99jlEREREYhTUs1G3bsw3PyCAg/mMpiQ4FE8WH3z0TXkF4+aXsnLLTgCOP/RAhvU4gr5d8+h/XB6HHJgbubCoKGFI99OPxVwYNxIN6UMfZ9ySsfvhE4iIiIjUpqCehXw+KA+7CGMoB14y3qzdUFpeEWL8g6W8snUXN/w4nwu6H0HHg3JrX1hUBDNmJHzGDKYQxkmsBaPBMrvwfcZNv7hZ5y4iIiJSFwX1LJSXB2ELYAnj5GC7M9VTahahsGXyonWs2LyTWy85lZ+e1rn2RX4/TJ0Ky5bVfu3IIyneO5Indw2LGzQMG24YN/24Zpu3iIiISEMoqGehnTvBYSxh68AQYl24O8yfn1WlL9Zafv+/9Tz75mf865gyfvy3a2DjRsjNrerI4nLBpk2JH1BYiH/4dCYNAhs37HRGzisSERERSTUF9Szk9UJOjiUQtFgczOMKRt97Dv1H+bMmrE977h0eLv2I4rJXGTrpBrC23nsqFRbC9OnMuAhCoaphY+Duu7PmfyIRERHJcDqZNAt5PDDmSieRtWJDkBx8wQGRVfUMt+OrMqY/9w5zlr7HHXtWc/adN3yvkO73w+LF1V8aNkznFYmIiEj60Ip6lurZE2KbI8M4OYTP4f4HM65N465vA/g27mD11l28snUXW7/4FoDpu1YxbO7NmIaGdGNg9uzKJD5jRrR7ZZTDoZIXERERSS8K6llq505wOAzhMBhCrKUXNngfJkPaNJYFQ9y3Yisr5j/FL5c/wq92vMc1BxzA5p9fyXGHHkjnGTcnXkk/4YTIQUbGRE4QLS+HH/wgksKjn7uoCJ54ovptF16YEf+ziIiISAuioJ6lInXqEAhE6tTv5wp+GZ5P/7y8VE+tTtZannrjU56e/Rhjnp7DhI/fqjyCyOyBI2/9Y+Ib8/Nh7tx603ai8460gVRERETSkWrUs5THA2PGxM7ZNARwMZ9R7Fq+KsUzS6wiFOap1z+h8HdzcF/yU2bN/jV9P34LB5HPYOq62eFoUEhPdN6RNpCKiIhIulJQz2KjRoHLFQYiq+r/v737D7KrrO84/v5uYoJJgSRCSdgkJBZE0yJiV8gKRooYsEWCI6NY2lAMXYEyotZsYrXDtNWRBKvoiEl2QCWt449BA4FWGZpSfsjCEErlRwImRIFAAkgWiEbya5/+cc5N7m7usr/YvWfvfb9mMrnnOSfnnJ0nT/LZZ7/nOd/lQh7+3gbW/fjWat/aPi/v2M3yO57g0suu4fCz5rLkq5dwxoZ79wX0XjU0wLJlfQrpld53tHChD5BKkqRisvSlhpVWf1m+vBNoYDejubvzPRz0ze/TfsRbOPedUzl03Bv6fL7OzsSm3/yWxgnjeOOYUV3ab37oWb5996+Ycdh4Pn7yTI6fNmHf/u2v7uaef7+Zidd8nWlP/ZIGYMe4g3nD3j28sjeY99sOWnZ07C9xqXTxgw+G7du7ts2ZA1deOeCQfv75sGRJn750SZKkYWdQr3HdV385nBf4wzF7+NQt61jys8c4848n89F3TaP5zW+ioSFIKfHrF3ewuWMHxzUeyoRxY0gpcdeG37DkZ4/x6LOvMH7MKM56+5F85F1T+d3OvVz508c4aO19fPbBG5ny3FNsnNjID+ddyKwPn0HHmjs4ccVVvP/pR7oG8G1bAWjMN3ucPY/Ipr2XLMneMrp0KTz7LCxY0Kep8J5Cer5KoyRJUmFF6s8a1HWkqakprV27ttq3MWhf/jJ84fOd+95S+gnaWNZwGc98+au0Hfs+Vj34DK+8uocjDz2IWUcewi82v8wL23cCWUY+9oiDGT92NA882cHUiW/kb949g18+t51bHtrCjl3Z24LOeGUT32r7NA1lbw9KQMfYg5mwc/u++qo+lbKUO+ecLqu19JchXZIkFV1EPJBSaqq4z6BeWa0E9fZ2OPW9e9m1O4vLY9nJ7fwZzaPuh7vu4tWmE7n10a3c8tAWnnj+t7x96qG8a+Ykpk8ax/899RL3/Wobz770e/66+Sj+8qTpjB2dlbz8buce/vPhLUz4xVpO+9fPM2r9+i7XLf9b1a+APnkyzJ49qIBe+rpPPvnAFRwN6ZIkqUgM6gNQK0Ed4JJLYPny7C2lDezhi/wjn+PKbMZ61ar+nay9ff8bTk84AS67DHbv7vufnzQJDjlk/xrnY8dCRweMHw+XX/66PNnZ3g4XXQTr1nVtN6RLkqSiea2gbo16Hehep/4Sh2Q7broJ2tr6Ho4XLYKrrqr8oqHelNeaD6H2dnjvew/83mHOHEO6JEkaWQzqdeDFF7OcnFIW1r/CQv6ITbSka+Hii7ODegvrld4U1F0ELF+efb76avj972H69OxlRPPnD8ti5UuXHhjSR43KFoeRJEkaSQzqdeDUU7OwumcPQNDJKC7lWxzHIzSne+ETn4Annuh5yrnSm4K6K4X0UuCvwuLk7e2wevWBt+ULjSRJ0kjkC4/qQHMzXHNNFlqzxzyDvYxmKQv3H7R0KUyZktXJzJgBM2dmn6dMqbx0SrmGhq4hvQpKdemdnfvbun/vIEmSNJL4MGkPaulh0pIPfQhuvLG0lWhgL3fzHpq5t38nmjsXxo2Dxx+HY48d9Aotg9VTXfpAnpWVJEkaTj5MKiDL06tXl2adsxKYpSxkFR/u30kK9lTm4sWV69JbW6tzP5IkSa8HS1/qSHMznH1217abOZt2ZvftBAUL6aWZ9Dvv7NpuXbokSaoFzqjXmdZWuPlmyF4iGnTGaFbO+wnNXJqVspTWNY/I1jp/ndc4f720tWXrw5fXpJdYly5JkmqBQb3ONDdns82XXpqF9ZTguv+Ywvw7Vo2YGei2tmxVyUqPV7S2GtIlSVJtsPSlDrW0wAc/uH979+7eF3YpikWLstUkewrpBarMkSRJGhSDep2aPLnr9urVWc13UZXq0St9QxEBK1YY0iVJUm0xqNep+fOz5c9LOjuLO6ve1gannHLgQ6NQiCXcJUmShoRBvU5VWgHmppuyUFwkpXr0Sg+NzpkDd99tSJckSbXJoF7HWluz9cZLUsoeMi1CCUyp1OW16tHvuMMlGCVJUu0yqNex0gowEfvb9u6tbglMe3v2BtV3v7tyqYv16JIkqV4Y1OtcSwvMm9e1rVolMIsWwcknw403Vt5vPbokSaonBnVVvQSmrQ1mzMhm8iuVuYD16JIkqf4Y1NVjCczixUN3zfb27M2ib3lLVof+5JOVj4uwHl2SJNUng7qAyiUwd96ZlaO8ntraYNasrMRl+XLYsKHnY885B37+c+vRJUlSfTKoa5/W1q6z6gBXXTX4evW2NjjpJJg2LZs9X7++5xIXyMpc7rkHVq1yFl2SJNWv0dW+ARVHczMsXNh11ZeUsnXMoX/14e3tsHIlrFnz2rPm5Y4/HpYtM5xLkiSBQV3dlMpMuof1Sy6B447rW4hua8uOr/SSokpmzYLLL/dBUUmSpHKWvugAS5Zk9eHlOjvhoot6Xwnmtd4kWu7oo7Pj7rkHHn3UkC5JktSdM+qqqLUVVq/uGrjXrYNTTsnKUwCuuw527YKdO2HsWNi6NfvVk8bG7NeCBQZzSZKk3hjUVVFzcxbIL76464OfnZ3ZA6H9YWmLJElS/w2q9CUiJkXEbRGxIf99Yg/HXZAfsyEiLihr/9OIeDgiNkbENyKyNUd6Om9EvDUi2iNiZ0R8tts1zoyIx/NzLS5rnxkR9+XtP4yIMYP5mutJS0u2hGL3lWD6qqEBVqywtEWSJGkgBlujvhhYk1I6BliTb3cREZOAK4CTgBOBK8oC/TLgb4Fj8l9n9nLebcAnga90u8Yo4BrgA8As4GMRMSvfvQT4WkrpaKADWDDIr7muDDSs+yZRSZKkwRlsUJ8HXJ9/vh44p8IxZwC3pZS2pZQ6gNuAMyNiCnBISunelFICVpb9+YrnTSk9n1K6H9jd7RonAhtTSptSSruAHwDz8hn604AberlHvYZSWG+o8LelsRHe9jZ4xzvgqKOyMpcVK3yTqCRJ0mANtkb9iJTSlvzzVuCICsc0Ak+XbW/O2xrzz93b+3re3q5xEvAm4KWU0p4K1zhARLQALQDTp0/v5ZL1paUlW55x5crsodJXX/WhUEmSpKHUa1CPiP8CJlfY9fnyjZRSiojXeN/kwAzVeXu4VhvQBtDU1DQs1xxJmpudJZckSRouvQb1lNLpPe2LiOciYkpKaUteyvJ8hcOeAU4t254K/E/ePrVb+zP5576ct/s1plU414vAhIgYnc+ql19DkiRJKqzB1qivBkqruFwA3FThmFuBuRExMX+IdC5wa17a8kpEzM5ryeeX/fm+nLfc/cAx+QovY4DzgNV57fvtwLn9OJckSZJUdYMN6lcC74+IDcDp+TYR0RQR1wKklLYB/0IWpu8H/jlvA7gUuBbYCDwB/LSX806OiM3AZ4AvRMTmiDgkny2/jOybgvXAj1JKj+bnWgR8JiI2ktWsXzfIr1mSJEkacpGSpdiVNDU1pbVr11b7NiRJklTDIuKBlFJTpX2DnVGXJEmSNAQM6pIkSVIBGdQlSZKkAjKoS5IkSQVkUJckSZIKyKAuSZIkFZBBXZIkSSogg7okSZJUQAZ1SZIkqYAM6pIkSVIBGdQlSZKkAjKoS5IkSQVkUJckSZIKyKAuSZIkFZBBXZIkSSogg7okSZJUQAZ1SZIkqYAipVTteyikiHgBeLIKlz4M+E0VrqvhZT/XB/u59tnH9cF+rg/V6uejUkqHV9phUC+YiFibUmqq9n1oaNnP9cF+rn32cX2wn+tDEfvZ0hdJkiSpgAzqkiRJUgEZ1Iunrdo3oGFhP9cH+7n22cf1wX6uD4XrZ2vUJUmSpAJyRl2SJEkqIIO6JEmSVEAG9QKJiDMj4vGI2BgRi6t9PxqYiJgWEbdHxLqIeDQiLs/bJ0XEbRGxIf99Yt4eEfGNvN8fioh3VvcrUH9ExKiIeDAibsm3Z0bEfXl//jAixuTtY/Ptjfn+GdW8b/VdREyIiBsi4rGIWB8RzY7n2hIRn87/vX4kIr4fEQc5lmtDRHw7Ip6PiEfK2vo9fiPigvz4DRFxwXDdv0G9ICJiFHAN8AFgFvCxiJhV3bvSAO0B/j6lNAuYDfxd3peLgTUppWOANfk2ZH1+TP6rBVg2/LesQbgcWF+2vQT4WkrpaKADWJC3LwA68vav5cdpZPg68LOU0luB48n62/FcIyKiEfgk0JRS+hNgFHAejuVa8V3gzG5t/Rq/ETEJuAI4CTgRuKIU7oeaQb04TgQ2ppQ2pZR2AT8A5lX5njQAKaUtKaX/zT9vJ/tPvZGsP6/PD7seOCf/PA9YmTL3AhMiYsow37YGICKmAn8BXJtvB3AacEN+SPd+LvX/DcD78uNVYBFxKDAHuA4gpbQrpfQSjudaMxp4Y0SMBsYBW3As14SU0p3Atm7N/R2/ZwC3pZS2pZQ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make plots - Reynolds stresses\n", + "plt.figure(figsize=(12,10))\n", + "plt.plot(dsm_data[:,4],label='DSM',linewidth=4)\n", + "plt.plot(ml_data[:,4],'ro',markersize=3,label='ML')\n", + "plt.plot(uns_data[:,4],'bo',markersize=3,label='UNS')\n", + "plt.legend()\n", + "plt.title('Rxx')\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "plt.plot(dsm_data[:,5],label='DSM')\n", + "plt.plot(ml_data[:,5],'ro',markersize=3,label='ML')\n", + "plt.plot(uns_data[:,5],'bo',markersize=3,label='UNS')\n", + "plt.legend()\n", + "plt.title('Ryy')\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "plt.plot(dsm_data[:,6],label='DSM')\n", + "plt.plot(ml_data[:,6],'ro',markersize=3,label='ML')\n", + "plt.plot(uns_data[:,6],'bo',markersize=3,label='UNS')\n", + "plt.legend()\n", + "plt.title('Rzz')\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "plt.plot(dsm_data[:,7],label='DSM')\n", + "plt.plot(ml_data[:,7],'ro',markersize=3,label='ML')\n", + "plt.plot(uns_data[:,7],'bo',markersize=3,label='UNS')\n", + "plt.legend()\n", + "plt.title('Rxy')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error due to ML: 0.008301148499999998 Error due to no model: 0.0185938405\n", + "Error due to ML: 0.0026460942454999995 Error due to no model: 0.0048548652905\n", + "Error due to ML: 0.0025481001 Error due to no model: 0.00524449995\n", + "Error due to ML: 0.0035513259109999995 Error due to no model: 0.004304335090550001\n" + ] + } + ], + "source": [ + "# Quantitative - Rxx\n", + "ml_error = np.sum(np.abs(dsm_data[:,4]-ml_data[:,4]))\n", + "uns_error = np.sum(np.abs(dsm_data[:,4]-uns_data[:,4]))\n", + "print('Error due to ML:',ml_error, ' Error due to no model:', uns_error)\n", + "\n", + "# Quantitative - Ryy\n", + "ml_error = np.sum(np.abs(dsm_data[:,5]-ml_data[:,5]))\n", + "uns_error = np.sum(np.abs(dsm_data[:,5]-uns_data[:,5]))\n", + "print('Error due to ML:',ml_error, ' Error due to no model:', uns_error)\n", + "\n", + "# Quantitative - Rzz\n", + "ml_error = np.sum(np.abs(dsm_data[:,6]-ml_data[:,6]))\n", + "uns_error = np.sum(np.abs(dsm_data[:,6]-uns_data[:,6]))\n", + "print('Error due to ML:',ml_error, ' Error due to no model:', uns_error)\n", + "\n", + "# Quantitative - Rxy - important\n", + "ml_error = np.sum(np.abs(dsm_data[:,7]-ml_data[:,7]))\n", + "uns_error = np.sum(np.abs(dsm_data[:,7]-uns_data[:,7]))\n", + "print('Error due to ML:',ml_error, ' Error due to no model:', uns_error)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/0/U b/ML_LES/Dyn_Smag/D_SMA_Case/0/U new file mode 100755 index 0000000..f9cc26b --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/0/U @@ -0,0 +1,60073 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volVectorField; + location "1000"; + object U; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + 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OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object k; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -2 0 0 0 0]; + +internalField nonuniform List +60000 +( +3.94306e-05 +3.00853e-05 +2.21131e-05 +1.54717e-05 +1.07369e-05 +8.81747e-06 +7.99254e-06 +7.42562e-06 +7.49022e-06 +7.88175e-06 +8.07891e-06 +7.94069e-06 +7.74516e-06 +7.58776e-06 +7.58382e-06 +7.84634e-06 +7.96475e-06 +7.8596e-06 +8.30752e-06 +9.39545e-06 +1.04101e-05 +1.11348e-05 +1.12369e-05 +1.02644e-05 +9.93262e-06 +1.0802e-05 +1.11418e-05 +1.08422e-05 +1.10364e-05 +1.24324e-05 +1.3836e-05 +1.41364e-05 +1.46743e-05 +1.64341e-05 +1.89988e-05 +2.18593e-05 +2.58045e-05 +3.22607e-05 +4.06512e-05 +4.47386e-05 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inout1_half0 + { + type cyclic; + } + inout1_half1 + { + type cyclic; + } + inout2_half0 + { + type cyclic; + } + inout2_half1 + { + type cyclic; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/0/nuTilda b/ML_LES/Dyn_Smag/D_SMA_Case/0/nuTilda new file mode 100755 index 0000000..a8ddf50 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/0/nuTilda @@ -0,0 +1,69 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5 | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1"; + object nuTilda; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -1 0 0 0 0]; + +internalField uniform 0; + +boundaryField +{ + bottomWall + { + type fixedValue; + value uniform 0; + } + topWall + { + type fixedValue; + value uniform 0; + } + sides1_half0 + { + type cyclic; + } + sides2_half0 + { + type cyclic; + } + inout1_half0 + { + type cyclic; + } + inout2_half0 + { + type cyclic; + } + sides2_half1 + { + type cyclic; + } + sides1_half1 + { + type cyclic; + } + inout1_half1 + { + type cyclic; + } + inout2_half1 + { + type cyclic; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/0/nut b/ML_LES/Dyn_Smag/D_SMA_Case/0/nut new file mode 100755 index 0000000..58fc5b3 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/0/nut @@ -0,0 +1,60071 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object nut; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -1 0 0 0 0]; + +internalField nonuniform List +60000 +( +3.21326e-08 +2.2299e-07 +3.32864e-07 +5.10662e-07 +6.43821e-07 +4.78013e-07 +1.73643e-07 +-1.16484e-07 +-2.77889e-07 +-2.47343e-07 +-5.67778e-08 +1.06284e-07 +1.31505e-07 +1.3946e-07 +1.18022e-07 +7.5891e-09 +-1.13711e-07 +-1.55148e-07 +-6.14348e-08 +5.1394e-08 +6.17946e-08 +6.10477e-08 +9.31066e-08 +4.88387e-08 +-7.25943e-08 +-1.23497e-07 +-1.22252e-07 +-1.33813e-07 +-1.32845e-07 +-1.22129e-07 +-7.87671e-08 +-3.62746e-08 +-4.92269e-08 +-1.11955e-07 +-2.17449e-07 +-3.27347e-07 +-4.12198e-07 +-4.47011e-07 +-3.74017e-07 +-1.92194e-07 +1.72674e-07 +5.01136e-07 +5.99616e-07 +9.08963e-07 +1.20486e-06 +8.47774e-07 +1.95207e-07 +-3.40871e-07 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+++ b/ML_LES/Dyn_Smag/D_SMA_Case/0/p @@ -0,0 +1,60071 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object p; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -2 0 0 0 0]; + +internalField nonuniform List +60000 +( +-0.000168142 +-0.000188879 +-0.000160074 +-0.000167958 +-9.48166e-05 +3.42481e-06 +1.94088e-05 +-5.01153e-05 +-0.000108898 +-9.51079e-05 +-4.80022e-05 +3.00146e-05 +8.13443e-05 +5.30315e-05 +2.83723e-05 +3.14489e-05 +-1.50135e-05 +-9.49708e-05 +-0.000103338 +1.40628e-08 +9.07318e-05 +0.000128243 +0.000119997 +2.8669e-05 +-8.33526e-05 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b/ML_LES/Dyn_Smag/D_SMA_Case/constant/fvOptions @@ -0,0 +1,33 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object fvOptions; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +momentumSource +{ + type meanVelocityForce; + active yes; + + meanVelocityForceCoeffs + { + selectionMode all; + + fields (U); + Ubar (0.1335 0 0); + } +} + + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/constant/polyMesh/boundary b/ML_LES/Dyn_Smag/D_SMA_Case/constant/polyMesh/boundary new file mode 100755 index 0000000..cb75ec9 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/constant/polyMesh/boundary @@ -0,0 +1,116 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class polyBoundaryMesh; + location "constant/polyMesh"; + object boundary; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +10 +( + bottomWall + { + type wall; + inGroups 1(wall); + nFaces 1200; + startFace 175300; + } + topWall + { + type wall; + inGroups 1(wall); + nFaces 1200; + startFace 176500; + } + sides1_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 177700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides1_half1; + } + sides1_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 178700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides1_half0; + } + sides2_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 179700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides2_half1; + } + sides2_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 180700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides2_half0; + } + inout1_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 181700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout1_half1; + } + inout1_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 182450; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout1_half0; + } + inout2_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 183200; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout2_half1; + } + inout2_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 183950; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout2_half0; + } +) + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/constant/polyMesh/faces b/ML_LES/Dyn_Smag/D_SMA_Case/constant/polyMesh/faces new file mode 100755 index 0000000..bfb140c --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/constant/polyMesh/faces @@ -0,0 +1,184724 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class faceList; + location "constant/polyMesh"; + 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| +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class labelList; + note "nPoints: 64821 nCells: 60000 nFaces: 184700 nInternalFaces: 175300"; + location "constant/polyMesh"; + object owner; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +184700 +( +0 +0 +0 +1 +1 +1 +2 +2 +2 +3 +3 +3 +4 +4 +4 +5 +5 +5 +6 +6 +6 +7 +7 +7 +8 +8 +8 +9 +9 +9 +10 +10 +10 +11 +11 +11 +12 +12 +12 +13 +13 +13 +14 +14 +14 +15 +15 +15 +16 +16 +16 +17 +17 +17 +18 +18 +18 +19 +19 +19 +20 +20 +20 +21 +21 +21 +22 +22 +22 +23 +23 +23 +24 +24 +24 +25 +25 +25 +26 +26 +26 +27 +27 +27 +28 +28 +28 +29 +29 +29 +30 +30 +30 +31 +31 +31 +32 +32 +32 +33 +33 +33 +34 +34 +34 +35 +35 +35 +36 +36 +36 +37 +37 +37 +38 +38 +38 +39 +39 +40 +40 +40 +41 +41 +41 +42 +42 +42 +43 +43 +43 +44 +44 +44 +45 +45 +45 +46 +46 +46 +47 +47 +47 +48 +48 +48 +49 +49 +49 +50 +50 +50 +51 +51 +51 +52 +52 +52 +53 +53 +53 +54 +54 +54 +55 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b/ML_LES/Dyn_Smag/D_SMA_Case/constant/polyMesh/points @@ -0,0 +1,64845 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class vectorField; + location "constant/polyMesh"; + object points; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +64821 +( +(0 0 0) +(0.1 0 0) +(0.2 0 0) +(0.3 0 0) +(0.4 0 0) +(0.5 0 0) +(0.6 0 0) +(0.7 0 0) +(0.8 0 0) +(0.9 0 0) +(1 0 0) +(1.1 0 0) +(1.2 0 0) +(1.3 0 0) +(1.4 0 0) +(1.5 0 0) +(1.6 0 0) +(1.7 0 0) +(1.8 0 0) +(1.9 0 0) +(2 0 0) +(2.1 0 0) +(2.2 0 0) +(2.3 0 0) +(2.4 0 0) +(2.5 0 0) +(2.6 0 0) +(2.7 0 0) +(2.8 0 0) +(2.9 0 0) +(3 0 0) +(3.1 0 0) +(3.2 0 0) +(3.3 0 0) +(3.4 0 0) 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1.968114097 2) +(3 1.968114097 2) +(3.1 1.968114097 2) +(3.2 1.968114097 2) +(3.3 1.968114097 2) +(3.4 1.968114097 2) +(3.5 1.968114097 2) +(3.6 1.968114097 2) +(3.7 1.968114097 2) +(3.8 1.968114097 2) +(3.9 1.968114097 2) +(4 1.968114097 2) +(0 1.979808232 2) +(0.1 1.979808232 2) +(0.2 1.979808232 2) +(0.3 1.979808232 2) +(0.4 1.979808232 2) +(0.5 1.979808232 2) +(0.6 1.979808232 2) +(0.7 1.979808232 2) +(0.8 1.979808232 2) +(0.9 1.979808232 2) +(1 1.979808232 2) +(1.1 1.979808232 2) +(1.2 1.979808232 2) +(1.3 1.979808232 2) +(1.4 1.979808232 2) +(1.5 1.979808232 2) +(1.6 1.979808232 2) +(1.7 1.979808232 2) +(1.8 1.979808232 2) +(1.9 1.979808232 2) +(2 1.979808232 2) +(2.1 1.979808232 2) +(2.2 1.979808232 2) +(2.3 1.979808232 2) +(2.4 1.979808232 2) +(2.5 1.979808232 2) +(2.6 1.979808232 2) +(2.7 1.979808232 2) +(2.8 1.979808232 2) +(2.9 1.979808232 2) +(3 1.979808232 2) +(3.1 1.979808232 2) +(3.2 1.979808232 2) +(3.3 1.979808232 2) +(3.4 1.979808232 2) +(3.5 1.979808232 2) +(3.6 1.979808232 2) +(3.7 1.979808232 2) +(3.8 1.979808232 2) +(3.9 1.979808232 2) +(4 1.979808232 2) +(0 1.990402377 2) +(0.1 1.990402377 2) +(0.2 1.990402377 2) +(0.3 1.990402377 2) +(0.4 1.990402377 2) +(0.5 1.990402377 2) +(0.6 1.990402377 2) +(0.7 1.990402377 2) +(0.8 1.990402377 2) +(0.9 1.990402377 2) +(1 1.990402377 2) +(1.1 1.990402377 2) +(1.2 1.990402377 2) +(1.3 1.990402377 2) +(1.4 1.990402377 2) +(1.5 1.990402377 2) +(1.6 1.990402377 2) +(1.7 1.990402377 2) +(1.8 1.990402377 2) +(1.9 1.990402377 2) +(2 1.990402377 2) +(2.1 1.990402377 2) +(2.2 1.990402377 2) +(2.3 1.990402377 2) +(2.4 1.990402377 2) +(2.5 1.990402377 2) +(2.6 1.990402377 2) +(2.7 1.990402377 2) +(2.8 1.990402377 2) +(2.9 1.990402377 2) +(3 1.990402377 2) +(3.1 1.990402377 2) +(3.2 1.990402377 2) +(3.3 1.990402377 2) +(3.4 1.990402377 2) +(3.5 1.990402377 2) +(3.6 1.990402377 2) +(3.7 1.990402377 2) +(3.8 1.990402377 2) +(3.9 1.990402377 2) +(4 1.990402377 2) +(0 2 2) +(0.1 2 2) +(0.2 2 2) +(0.3 2 2) +(0.4 2 2) +(0.5 2 2) +(0.6 2 2) +(0.7 2 2) +(0.8 2 2) +(0.9 2 2) +(1 2 2) +(1.1 2 2) +(1.2 2 2) +(1.3 2 2) +(1.4 2 2) +(1.5 2 2) +(1.6 2 2) +(1.7 2 2) +(1.8 2 2) +(1.9 2 2) +(2 2 2) +(2.1 2 2) +(2.2 2 2) +(2.3 2 2) +(2.4 2 2) +(2.5 2 2) +(2.6 2 2) +(2.7 2 2) +(2.8 2 2) +(2.9 2 2) +(3 2 2) +(3.1 2 2) +(3.2 2 2) +(3.3 2 2) +(3.4 2 2) +(3.5 2 2) +(3.6 2 2) +(3.7 2 2) +(3.8 2 2) +(3.9 2 2) +(4 2 2) +) + + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/constant/postChannelDict b/ML_LES/Dyn_Smag/D_SMA_Case/constant/postChannelDict new file mode 100755 index 0000000..e1a5d5c --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/constant/postChannelDict @@ -0,0 +1,29 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object postChannelDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +// Seed patches to start layering from +patches ( bottomWall ); + +// Direction in which the layers are +component y; + +// Is the mesh symmetric? If so average(symmetric fields) or +// subtract(asymmetric) contributions from both halves +symmetric true; + + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/constant/transportProperties b/ML_LES/Dyn_Smag/D_SMA_Case/constant/transportProperties new file mode 100755 index 0000000..2f0f1ec --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/constant/transportProperties @@ -0,0 +1,24 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object transportProperties; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +Ubar [0 1 -1 0 0 0 0] (0.1335 0 0); + +transportModel Newtonian; + +nu [0 2 -1 0 0 0 0] 2e-05; + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/constant/turbulenceProperties b/ML_LES/Dyn_Smag/D_SMA_Case/constant/turbulenceProperties new file mode 100755 index 0000000..7d58884 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/constant/turbulenceProperties @@ -0,0 +1,100 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object turbulenceProperties; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +simulationType LES; + +LES +{ + LESModel D_SMA; + + turbulence on; + + printCoeffs on; + + delta cubeRootVol; + + filter simple; + + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + dynamicSmagorinskyCoeffs + { + filter simple; + } + + PrandtlCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } + + Cdelta 0.158; + } + + vanDriestCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } + + Aplus 26; + Cdelta 0.158; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/system/blockMeshDict b/ML_LES/Dyn_Smag/D_SMA_Case/system/blockMeshDict new file mode 100755 index 0000000..26ae6ee --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/system/blockMeshDict @@ -0,0 +1,115 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + object blockMeshDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +convertToMeters 1; + +vertices +( + (0 0 0) + (4 0 0) + (0 1 0) + (4 1 0) + (0 2 0) + (4 2 0) + (0 0 2) + (4 0 2) + (0 1 2) + (4 1 2) + (0 2 2) + (4 2 2) +); + +blocks +( + hex (0 1 3 2 6 7 9 8) (40 25 30) simpleGrading (1 10.7028 1) + hex (2 3 5 4 8 9 11 10) (40 25 30) simpleGrading (1 0.0934 1) +); + +edges +( +); + +boundary +( + bottomWall + { + type wall; + faces ((0 1 7 6)); + } + topWall + { + type wall; + faces ((4 10 11 5)); + } + + sides1_half0 + { + type cyclic; + neighbourPatch sides1_half1; + faces ((0 2 3 1)); + } + sides1_half1 + { + type cyclic; + neighbourPatch sides1_half0; + faces ((6 7 9 8)); + } + + sides2_half0 + { + type cyclic; + neighbourPatch sides2_half1; + faces ((2 4 5 3)); + } + sides2_half1 + { + type cyclic; + neighbourPatch sides2_half0; + faces ((8 9 11 10)); + } + + inout1_half0 + { + type cyclic; + neighbourPatch inout1_half1; + faces ((1 3 9 7)); + } + inout1_half1 + { + type cyclic; + neighbourPatch inout1_half0; + faces ((0 6 8 2)); + } + + inout2_half0 + { + type cyclic; + neighbourPatch inout2_half1; + faces ((3 5 11 9)); + } + inout2_half1 + { + type cyclic; + neighbourPatch inout2_half0; + faces ((2 8 10 4)); + } +); + +mergePatchPairs +( +); + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/system/controlDict b/ML_LES/Dyn_Smag/D_SMA_Case/system/controlDict new file mode 100755 index 0000000..9037f8f --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/system/controlDict @@ -0,0 +1,80 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object controlDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +libs +( +"D_SMA.so" +); + +application pimpleFoam; + +startFrom startTime; + +startTime 0; + +stopAt endTime; + +endTime 1000; + +deltaT 0.2; + +writeControl timeStep; + +writeInterval 200; + +purgeWrite 0; + +writeFormat ascii; + +writePrecision 6; + +writeCompression off; + +timeFormat general; + +timePrecision 6; + +runTimeModifiable true; + +functions +{ + fieldAverage1 + { + type fieldAverage; + libs ("libfieldFunctionObjects.so"); + writeControl writeTime; + + fields + ( + U + { + mean on; + prime2Mean on; + base time; + } + + p + { + mean on; + prime2Mean on; + base time; + } + ); + } +} + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/system/decomposeParDict b/ML_LES/Dyn_Smag/D_SMA_Case/system/decomposeParDict new file mode 100755 index 0000000..b469469 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/system/decomposeParDict @@ -0,0 +1,135 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + note "mesh decomposition control dictionary"; + object decomposeParDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +numberOfSubdomains 4; + +//- Keep owner and neighbour on same processor for faces in zones: +// preserveFaceZones (heater solid1 solid3); + +//- Keep owner and neighbour on same processor for faces in patches: +// (makes sense only for cyclic patches) +//preservePatches (cyclic_half0 cyclic_half1); + +//- Keep all of faceSet on a single processor. This puts all cells +// connected with a point, edge or face on the same processor. +// (just having face connected cells might not guarantee a balanced +// decomposition) +// The processor can be -1 (the decompositionMethod chooses the processor +// for a good load balance) or explicitly provided (upsets balance). +//singleProcessorFaceSets ((f0 -1)); + + +//- Use the volScalarField named here as a weight for each cell in the +// decomposition. For example, use a particle population field to decompose +// for a balanced number of particles in a lagrangian simulation. +// weightField dsmcRhoNMean; + +// method scotch; +// method hierarchical; +method simple; +// method metis; +// method manual; +// method multiLevel; +// method structured; // does 2D decomposition of structured mesh + +multiLevelCoeffs +{ + // Decomposition methods to apply in turn. This is like hierarchical but + // fully general - every method can be used at every level. + + level0 + { + numberOfSubdomains 64; + //method simple; + //simpleCoeffs + //{ + // n (2 1 1); + // delta 0.001; + //} + method scotch; + } + level1 + { + numberOfSubdomains 4; + method scotch; + } +} + +// Desired output + +simpleCoeffs +{ + n (1 2 2); + delta 0.001; +} + +hierarchicalCoeffs +{ + n (1 2 1); + delta 0.001; + order xyz; +} + +metisCoeffs +{ + /* + processorWeights + ( + 1 + 1 + 1 + 1 + ); + */ +} + +scotchCoeffs +{ + //processorWeights + //( + // 1 + // 1 + // 1 + // 1 + //); + //writeGraph true; + //strategy "b"; +} + +manualCoeffs +{ + dataFile "decompositionData"; +} + +structuredCoeffs +{ + // Patches to do 2D decomposition on. Structured mesh only; cells have + // to be in 'columns' on top of patches. + patches (bottomPatch); +} + +//// Is the case distributed? Note: command-line argument -roots takes +//// precedence +//distributed yes; +//// Per slave (so nProcs-1 entries) the directory above the case. +//roots +//( +// "/tmp" +// "/tmp" +//); + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/system/fvSchemes b/ML_LES/Dyn_Smag/D_SMA_Case/system/fvSchemes new file mode 100755 index 0000000..78fc3ba --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/system/fvSchemes @@ -0,0 +1,60 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object fvSchemes; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +ddtSchemes +{ + default backward; +} + +gradSchemes +{ + default Gauss linear; +} + +divSchemes +{ + default none; + div(phi,U) Gauss linear; + div(phi,k) Gauss limitedLinear 1; + div(phi,B) Gauss limitedLinear 1; + div(B) Gauss linear; + div(phi,nuTilda) Gauss limitedLinear 1; + div((nuEff*dev2(T(grad(U))))) Gauss linear; +} + +laplacianSchemes +{ + default Gauss linear corrected; +} + +interpolationSchemes +{ + default linear; +} + +snGradSchemes +{ + default corrected; +} + +wallDist +{ + method meshWave; +} + + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Case/system/fvSolution b/ML_LES/Dyn_Smag/D_SMA_Case/system/fvSolution new file mode 100755 index 0000000..348f3e5 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Case/system/fvSolution @@ -0,0 +1,62 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object fvSolution; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +solvers +{ + p + { + solver GAMG; + tolerance 0; + relTol 0.1; + smoother GaussSeidel; + } + + pFinal + { + $p; + smoother DICGaussSeidel; + tolerance 1e-06; + relTol 0; + } + + "(U|k|nuTilda)" + { + solver smoothSolver; + smoother symGaussSeidel; + tolerance 1e-05; + relTol 0.1; + } + + "(U|k|nuTilda)Final" + { + $U; + tolerance 1e-05; + relTol 0; + } +} + +PIMPLE +{ + nOuterCorrectors 1; + nCorrectors 2; + nNonOrthogonalCorrectors 0; + pRefCell 1001; + pRefValue 0; +} + + +// ************************************************************************* // diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/D_SMA.C b/ML_LES/Dyn_Smag/D_SMA_Model/D_SMA.C new file mode 100755 index 0000000..2180e81 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Model/D_SMA.C @@ -0,0 +1,238 @@ +/*---------------------------------------------------------------------------*\ + ========= | + \\ / F ield | OpenFOAM: The Open Source CFD Toolbox + \\ / O peration | + \\ / A nd | Copyright (C) 2011-2015 OpenFOAM Foundation + \\/ M anipulation | +------------------------------------------------------------------------------- +License + This file is part of OpenFOAM. + + OpenFOAM is free software: you can redistribute it and/or modify it + under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + OpenFOAM is distributed in the hope that it will be useful, but WITHOUT + ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or + FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + for more details. + + You should have received a copy of the GNU General Public License + along with OpenFOAM. If not, see . + +\*---------------------------------------------------------------------------*/ + +#include "D_SMA.H" +#include "fvOptions.H" +#include "wallDist.H" + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +namespace Foam +{ +namespace LESModels +{ + +// * * * * * * * * * * * * * Private Member Functions * * * * * * * * * * * // + +template +void D_SMA::correctNut +( + const tmp& gradU +) +{ + this->nut_ = max(Cs_*sqr(this->delta())*mag(dev(symm(gradU))),-1.0*this->nu()); + this->nut_.correctBoundaryConditions(); + fv::options::New(this->mesh_).correct(this->nut_); + + BasicTurbulenceModel::correctNut(); +} + + +template +void D_SMA::correctNut() +{ + correctNut(fvc::grad(this->U_)); +} + + +// * * * * * * * * * * * * * * * * Constructors * * * * * * * * * * * * * * // + +template +D_SMA::D_SMA +( + const alphaField& alpha, + const rhoField& rho, + const volVectorField& U, + const surfaceScalarField& alphaRhoPhi, + const surfaceScalarField& phi, + const transportModel& transport, + const word& propertiesName, + const word& type +) +: + LESeddyViscosity + ( + type, + alpha, + rho, + U, + alphaRhoPhi, + phi, + transport, + propertiesName + ), + + k_ + ( + IOobject + ( + IOobject::groupName("k", this->U_.group()), + this->runTime_.timeName(), + this->mesh_, + IOobject::MUST_READ, + IOobject::AUTO_WRITE + ), + this->mesh_ + ), + + Cs_ + ( + IOobject + ( + IOobject::groupName("Cs", this->U_.group()), + this->runTime_.timeName(), + this->mesh_, + IOobject::READ_IF_PRESENT, + IOobject::AUTO_WRITE + ), + this->mesh_, + dimensionedScalar ("Cs", dimless,SMALL) + ), + simpleFilter_(U.mesh()), + filterPtr_(LESfilter::New(U.mesh(), this->coeffDict())), + filter_(filterPtr_()), + y_(wallDist::New(this->mesh_).y()) +{ +// bound(k_, this->kMin_); + + if (type == typeName) + { + this->printCoeffs(type); + } +} + +// * * * * * * * * * * * * * * * Member Functions * * * * * * * * * * * * * // + +template +bool D_SMA::read() +{ + if (LESeddyViscosity::read()) + { + filter_.read(this->coeffDict()); + + return true; + } + else + { + return false; + } +} + + +template +void D_SMA::correct() +{ + if (!this->turbulence_) + { + return; + } + + // Local references + // const surfaceScalarField& phi = this->phi_; + const volVectorField& U = this->U_; + // fv::options& fvOptions(fv::options::New(this->mesh_)); // Source term + + LESeddyViscosity::correct(); // For changing mesh - coming from turbulenceModel.C through virtual shit + + tmp tgradU(fvc::grad(U)); + const volTensorField& gradU = tgradU(); + + volSymmTensorField S(dev(symm(gradU))); + volScalarField magS(mag(S)); + + volVectorField Uf(filter_(U)); + + volSymmTensorField Sf(filter_(S)); +// volSymmTensorField Sf(dev(symm(fvc::grad(Uf)))); + + volScalarField magSf(mag(Sf)); + + + volSymmTensorField LL = + (dev(filter_(sqr(U)) - (sqr(filter_(U))))); + + volSymmTensorField MM + ( + sqr(this->delta())*(filter_(magS*S) - 4.0*magSf*Sf) + ); + + volScalarField MMMM = fvc::average(magSqr(MM)); + MMMM.max(VSMALL); + + Cs_= 0.5* fvc::average(LL && MM)/MMMM; + + volScalarField KK = + 0.5*(filter_(magSqr(U)) - magSqr(filter_(U))); + + volScalarField mm + ( + sqr(this->delta())*(4.0*sqr(mag(Sf)) - filter_(sqr(magS))) + + ); + + volScalarField mmmm = fvc::average(magSqr(mm)); + mmmm.max(VSMALL); + + k_ = fvc::average(KK*mm)/mmmm * sqr(this->delta())*magSqr(S); + + correctNut(gradU); + + volScalarField filter_width(this->delta()); + + if(this->runTime_.outputTime()) + { + const char* Cs_name = "Cs"; + Cs_.rename(Cs_name); + Cs_.write(); + + const char* Uf_name = "Uf"; + Uf.rename(Uf_name); + Uf.write(); + + const char* S_name = "S_ij"; + S.rename(S_name); + S.write(); + + const char* delta_name = "del"; + filter_width.rename(delta_name); + filter_width.write(); + + const char* y_name = "yw"; + y_.rename(y_name); + y_.write(); + + const char* nut_name = "nut"; + this->nut_.rename(nut_name); + this->nut_.write(); + } +} + + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +} // End namespace LESModels +} // End namespace Foam + +// ************************************************************************* // \ No newline at end of file diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/D_SMA.H b/ML_LES/Dyn_Smag/D_SMA_Model/D_SMA.H new file mode 100755 index 0000000..bf0115f --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Model/D_SMA.H @@ -0,0 +1,168 @@ +/*---------------------------------------------------------------------------*\ + ========= | + \\ / F ield | OpenFOAM: The Open Source CFD Toolbox + \\ / O peration | + \\ / A nd | Copyright (C) 2011-2016 OpenFOAM Foundation + \\/ M anipulation | +------------------------------------------------------------------------------- +License + This file is part of OpenFOAM. + + OpenFOAM is free software: you can redistribute it and/or modify it + under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + OpenFOAM is distributed in the hope that it will be useful, but WITHOUT + ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or + FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + for more details. + + You should have received a copy of the GNU General Public License + along with OpenFOAM. If not, see . + +Class + Foam::LESModels::D_SMA + +Group + grpLESTurbulence + +Description + Dynamic SGS model with local averaging, based on Germano and Lilly works. + + An older version of this code had been developed for version 2.3.1 and older by Alberto Passalacqua. + + The current version has been developed for version 4.1 + + Ehsan Asgari, e.asgari@aut.ac.ir + +SourceFiles + D_SMA.C + +\*---------------------------------------------------------------------------*/ + +#ifndef D_SMA_H +#define D_SMA_H + +#include "LESeddyViscosity.H" +#include "simpleFilter.H" + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +namespace Foam +{ +namespace LESModels +{ + +/*---------------------------------------------------------------------------*\ + Class D_SMA Declaration +\*---------------------------------------------------------------------------*/ + +template +class D_SMA +: + public LESeddyViscosity +{ + // Private Member Functions + + // Disallow default bitwise copy construct and assignment + D_SMA(const D_SMA&); + void operator=(const D_SMA&); + + +protected: + + // Protected data + volScalarField k_; + volScalarField Cs_; + +// volScalarField beta_; + simpleFilter simpleFilter_; + autoPtr filterPtr_; + LESfilter& filter_; + volScalarField y_; + + + + + + // Protected Member Functions + + //- Update sub-grid eddy-viscosity + void correctNut(const tmp& gradU); + + virtual void correctNut(); + + +public: + + typedef typename BasicTurbulenceModel::alphaField alphaField; + typedef typename BasicTurbulenceModel::rhoField rhoField; + typedef typename BasicTurbulenceModel::transportModel transportModel; + + //- Runtime type information + TypeName("D_SMA"); + + + // Constructors + + //- Construct from components + D_SMA + ( + const alphaField& alpha, + const rhoField& rho, + const volVectorField& U, + const surfaceScalarField& alphaRhoPhi, + const surfaceScalarField& phi, + const transportModel& transport, + const word& propertiesName = turbulenceModel::propertiesName, + const word& type = typeName + ); + + + //- Destructor + virtual ~D_SMA() + {} + + + // Member Functions + + //- Read model coefficients if they have changed + virtual bool read(); + + //- Return SGS kinetic energy + virtual tmp k() const + { + return k_; + } + + //- Return the effective diffusivity for k + tmp DkEff() const + { + return tmp + ( + new volScalarField("DkEff", this->nut_ + this->nu()) + ); + } + + //- Correct Eddy-Viscosity and related properties + virtual void correct(); +}; + + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +} // End namespace LESModels +} // End namespace Foam + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +#ifdef NoRepository + #include "D_SMA.C" +#endif + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +#endif + +// ************************************************************************* // \ No newline at 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+$(WM_PROJECT_DIR)/src/finiteVolume/lnInclude/zeroGradientFvPatchFields.H \ +$(WM_PROJECT_DIR)/src/finiteVolume/lnInclude/zeroGradientFvPatchField.H \ +$(WM_PROJECT_DIR)/src/finiteVolume/lnInclude/zeroGradientFvPatchField.C \ +$(WM_PROJECT_DIR)/src/finiteVolume/lnInclude/patchDistMethodTemplates.C \ + + diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o new file mode 100755 index 0000000..6d0b6a5 Binary files /dev/null and b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o differ diff --git a/OF_Model/Make/linux64GccDPInt64Opt/options b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/options old mode 100644 new mode 100755 similarity index 100% rename from OF_Model/Make/linux64GccDPInt64Opt/options rename to ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/options diff --git a/OF_Model/Make/linux64GccDPInt64Opt/sourceFiles b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/sourceFiles old mode 100644 new mode 100755 similarity index 100% rename from OF_Model/Make/linux64GccDPInt64Opt/sourceFiles rename to ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/sourceFiles diff --git a/OF_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep old mode 100644 new mode 100755 similarity index 100% rename from OF_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep rename to ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/tf_utils.o b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/tf_utils.o new file mode 100755 index 0000000..65532fc Binary files /dev/null and b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/tf_utils.o differ diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/variables b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/variables new file mode 100755 index 0000000..9cd4a14 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Model/Make/linux64GccDPInt64Opt/variables @@ -0,0 +1 @@ +LIB = $(FOAM_USER_LIBBIN)/D_SMA diff --git a/OF_Model/Make/options b/ML_LES/Dyn_Smag/D_SMA_Model/Make/options old mode 100644 new mode 100755 similarity index 100% rename from OF_Model/Make/options rename to ML_LES/Dyn_Smag/D_SMA_Model/Make/options diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/D_SMA.C b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/D_SMA.C new file mode 120000 index 0000000..fae6256 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/D_SMA.C @@ -0,0 +1 @@ +../D_SMA.C \ No newline at end of file diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/D_SMA.H b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/D_SMA.H new file mode 120000 index 0000000..7fb09e7 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/D_SMA.H @@ -0,0 +1 @@ +../D_SMA.H \ No newline at end of file diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/maketurbulentTransportModels.C b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/maketurbulentTransportModels.C new file mode 120000 index 0000000..0b8de36 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/maketurbulentTransportModels.C @@ -0,0 +1 @@ +../maketurbulentTransportModels.C \ No newline at end of file diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/tf_utils.C b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/tf_utils.C new file mode 120000 index 0000000..28a9278 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/tf_utils.C @@ -0,0 +1 @@ +../tf_utils.C \ No newline at end of file diff --git a/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/tf_utils.H b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/tf_utils.H new file mode 120000 index 0000000..b478e26 --- /dev/null +++ b/ML_LES/Dyn_Smag/D_SMA_Model/lnInclude/tf_utils.H @@ -0,0 +1 @@ +../tf_utils.H \ No newline at end of file diff --git a/OF_Model/maketurbulentTransportModels.C b/ML_LES/Dyn_Smag/D_SMA_Model/maketurbulentTransportModels.C old mode 100644 new mode 100755 similarity index 96% rename from OF_Model/maketurbulentTransportModels.C rename to ML_LES/Dyn_Smag/D_SMA_Model/maketurbulentTransportModels.C index d4f90d8..3c10d9f --- a/OF_Model/maketurbulentTransportModels.C +++ b/ML_LES/Dyn_Smag/D_SMA_Model/maketurbulentTransportModels.C @@ -25,5 +25,5 @@ License #include "turbulentTransportModels.H" #include "simpleFilter.H" -#include "ML_SA_CG.H" -makeRASModel(ML_SA_CG); \ No newline at end of file +#include "D_SMA.H" +makeLESModel(D_SMA); \ No newline at end of file diff --git a/OF_Model/lnInclude/tf_utils.C b/ML_LES/Dyn_Smag/D_SMA_Model/tf_utils.C old mode 100644 new mode 100755 similarity index 100% rename from OF_Model/lnInclude/tf_utils.C rename to ML_LES/Dyn_Smag/D_SMA_Model/tf_utils.C diff --git a/OF_Model/lnInclude/tf_utils.H b/ML_LES/Dyn_Smag/D_SMA_Model/tf_utils.H old mode 100644 new mode 100755 similarity index 100% rename from OF_Model/lnInclude/tf_utils.H rename to ML_LES/Dyn_Smag/D_SMA_Model/tf_utils.H diff --git a/ML_LES/ML_LES/ML_LES_Case/0/U b/ML_LES/ML_LES/ML_LES_Case/0/U new file mode 100755 index 0000000..f9cc26b --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/0/U @@ -0,0 +1,60073 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volVectorField; + location "1000"; + object U; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 1 -1 0 0 0 0]; + +internalField nonuniform List +60000 +( +(0.0107864 0.000249703 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OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object k; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -2 0 0 0 0]; + +internalField nonuniform List +60000 +( +3.94306e-05 +3.00853e-05 +2.21131e-05 +1.54717e-05 +1.07369e-05 +8.81747e-06 +7.99254e-06 +7.42562e-06 +7.49022e-06 +7.88175e-06 +8.07891e-06 +7.94069e-06 +7.74516e-06 +7.58776e-06 +7.58382e-06 +7.84634e-06 +7.96475e-06 +7.8596e-06 +8.30752e-06 +9.39545e-06 +1.04101e-05 +1.11348e-05 +1.12369e-05 +1.02644e-05 +9.93262e-06 +1.0802e-05 +1.11418e-05 +1.08422e-05 +1.10364e-05 +1.24324e-05 +1.3836e-05 +1.41364e-05 +1.46743e-05 +1.64341e-05 +1.89988e-05 +2.18593e-05 +2.58045e-05 +3.22607e-05 +4.06512e-05 +4.47386e-05 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inout1_half0 + { + type cyclic; + } + inout1_half1 + { + type cyclic; + } + inout2_half0 + { + type cyclic; + } + inout2_half1 + { + type cyclic; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/0/nuTilda b/ML_LES/ML_LES/ML_LES_Case/0/nuTilda new file mode 100755 index 0000000..a8ddf50 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/0/nuTilda @@ -0,0 +1,69 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5 | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1"; + object nuTilda; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -1 0 0 0 0]; + +internalField uniform 0; + +boundaryField +{ + bottomWall + { + type fixedValue; + value uniform 0; + } + topWall + { + type fixedValue; + value uniform 0; + } + sides1_half0 + { + type cyclic; + } + sides2_half0 + { + type cyclic; + } + inout1_half0 + { + type cyclic; + } + inout2_half0 + { + type cyclic; + } + sides2_half1 + { + type cyclic; + } + sides1_half1 + { + type cyclic; + } + inout1_half1 + { + type cyclic; + } + inout2_half1 + { + type cyclic; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/0/nut b/ML_LES/ML_LES/ML_LES_Case/0/nut new file mode 100755 index 0000000..58fc5b3 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/0/nut @@ -0,0 +1,60071 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object nut; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -1 0 0 0 0]; + +internalField nonuniform List +60000 +( +3.21326e-08 +2.2299e-07 +3.32864e-07 +5.10662e-07 +6.43821e-07 +4.78013e-07 +1.73643e-07 +-1.16484e-07 +-2.77889e-07 +-2.47343e-07 +-5.67778e-08 +1.06284e-07 +1.31505e-07 +1.3946e-07 +1.18022e-07 +7.5891e-09 +-1.13711e-07 +-1.55148e-07 +-6.14348e-08 +5.1394e-08 +6.17946e-08 +6.10477e-08 +9.31066e-08 +4.88387e-08 +-7.25943e-08 +-1.23497e-07 +-1.22252e-07 +-1.33813e-07 +-1.32845e-07 +-1.22129e-07 +-7.87671e-08 +-3.62746e-08 +-4.92269e-08 +-1.11955e-07 +-2.17449e-07 +-3.27347e-07 +-4.12198e-07 +-4.47011e-07 +-3.74017e-07 +-1.92194e-07 +1.72674e-07 +5.01136e-07 +5.99616e-07 +9.08963e-07 +1.20486e-06 +8.47774e-07 +1.95207e-07 +-3.40871e-07 +-6.57385e-07 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+++ b/ML_LES/ML_LES/ML_LES_Case/0/p @@ -0,0 +1,60071 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object p; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -2 0 0 0 0]; + +internalField nonuniform List +60000 +( +-0.000168142 +-0.000188879 +-0.000160074 +-0.000167958 +-9.48166e-05 +3.42481e-06 +1.94088e-05 +-5.01153e-05 +-0.000108898 +-9.51079e-05 +-4.80022e-05 +3.00146e-05 +8.13443e-05 +5.30315e-05 +2.83723e-05 +3.14489e-05 +-1.50135e-05 +-9.49708e-05 +-0.000103338 +1.40628e-08 +9.07318e-05 +0.000128243 +0.000119997 +2.8669e-05 +-8.33526e-05 +-5.07719e-05 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+7.4067e-05,0.0068279,5.0944e-06,-0.0004088,4.1618e-05,5.3783e-08,4.9453e-06,6.574e-07,2.4562e-08,5.5768e-07,1,1.996,2,1.996,0.1 +7.4019e-05,0.003414,2.5472e-06,-0.0002044,2.0809e-05,2.6891e-08,2.4726e-06,3.287e-07,1.2281e-08,2.7884e-07,1,1.998,2,1.998,0.1 +7.397e-05,0,0,0,0,0,0,0,0,0,1,2,2,2,0.1 diff --git a/ML_LES/ML_LES/ML_LES_Case/ML_LES.pb b/ML_LES/ML_LES/ML_LES_Case/ML_LES.pb new file mode 100755 index 0000000..15160ef Binary files /dev/null and b/ML_LES/ML_LES/ML_LES_Case/ML_LES.pb differ diff --git a/ML_LES/ML_LES/ML_LES_Case/constant/fvOptions b/ML_LES/ML_LES/ML_LES_Case/constant/fvOptions new file mode 100755 index 0000000..bff25be --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/constant/fvOptions @@ -0,0 +1,33 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object fvOptions; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +momentumSource +{ + type meanVelocityForce; + active yes; + + meanVelocityForceCoeffs + { + selectionMode all; + + fields (U); + Ubar (0.1335 0 0); + } +} + + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/boundary b/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/boundary new file mode 100755 index 0000000..cb75ec9 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/boundary @@ -0,0 +1,116 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class polyBoundaryMesh; + location "constant/polyMesh"; + object boundary; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +10 +( + bottomWall + { + type wall; + inGroups 1(wall); + nFaces 1200; + startFace 175300; + } + topWall + { + type wall; + inGroups 1(wall); + nFaces 1200; + startFace 176500; + } + sides1_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 177700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides1_half1; + } + sides1_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 178700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides1_half0; + } + sides2_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 179700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides2_half1; + } + sides2_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 180700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides2_half0; + } + inout1_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 181700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout1_half1; + } + inout1_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 182450; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout1_half0; + } + inout2_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 183200; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout2_half1; + } + inout2_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 183950; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout2_half0; + } +) + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/faces b/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/faces new file mode 100755 index 0000000..bfb140c --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/faces @@ -0,0 +1,184724 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class faceList; + location "constant/polyMesh"; + object faces; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +184700 +( +4(1 42 1108 1067) +4(41 1107 1108 42) +4(1066 1067 1108 1107) +4(2 43 1109 1068) +4(42 1108 1109 43) +4(1067 1068 1109 1108) +4(3 44 1110 1069) +4(43 1109 1110 44) +4(1068 1069 1110 1109) +4(4 45 1111 1070) +4(44 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************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/neighbour b/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/neighbour new file mode 100755 index 0000000..f807274 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/constant/polyMesh/neighbour @@ -0,0 +1,175325 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class labelList; + note "nPoints: 64821 nCells: 60000 nFaces: 184700 nInternalFaces: 175300"; + location "constant/polyMesh"; + object neighbour; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +175300 +( +1 +40 +1000 +2 +41 +1001 +3 +42 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location "constant/polyMesh"; + object owner; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +184700 +( +0 +0 +0 +1 +1 +1 +2 +2 +2 +3 +3 +3 +4 +4 +4 +5 +5 +5 +6 +6 +6 +7 +7 +7 +8 +8 +8 +9 +9 +9 +10 +10 +10 +11 +11 +11 +12 +12 +12 +13 +13 +13 +14 +14 +14 +15 +15 +15 +16 +16 +16 +17 +17 +17 +18 +18 +18 +19 +19 +19 +20 +20 +20 +21 +21 +21 +22 +22 +22 +23 +23 +23 +24 +24 +24 +25 +25 +25 +26 +26 +26 +27 +27 +27 +28 +28 +28 +29 +29 +29 +30 +30 +30 +31 +31 +31 +32 +32 +32 +33 +33 +33 +34 +34 +34 +35 +35 +35 +36 +36 +36 +37 +37 +37 +38 +38 +38 +39 +39 +40 +40 +40 +41 +41 +41 +42 +42 +42 +43 +43 +43 +44 +44 +44 +45 +45 +45 +46 +46 +46 +47 +47 +47 +48 +48 +48 +49 +49 +49 +50 +50 +50 +51 +51 +51 +52 +52 +52 +53 +53 +53 +54 +54 +54 +55 +55 +55 +56 +56 +56 +57 +57 +57 +58 +58 +58 +59 +59 +59 +60 +60 +60 +61 +61 +61 +62 +62 +62 +63 +63 +63 +64 +64 +64 +65 +65 +65 +66 +66 +66 +67 +67 +67 +68 +68 +68 +69 +69 +69 +70 +70 +70 +71 +71 +71 +72 +72 +72 +73 +73 +73 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A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class vectorField; + location "constant/polyMesh"; + object points; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +64821 +( +(0 0 0) +(0.1 0 0) +(0.2 0 0) +(0.3 0 0) +(0.4 0 0) +(0.5 0 0) +(0.6 0 0) +(0.7 0 0) +(0.8 0 0) +(0.9 0 0) +(1 0 0) +(1.1 0 0) +(1.2 0 0) +(1.3 0 0) +(1.4 0 0) +(1.5 0 0) +(1.6 0 0) +(1.7 0 0) +(1.8 0 0) +(1.9 0 0) +(2 0 0) +(2.1 0 0) +(2.2 0 0) +(2.3 0 0) +(2.4 0 0) +(2.5 0 0) +(2.6 0 0) +(2.7 0 0) +(2.8 0 0) +(2.9 0 0) +(3 0 0) +(3.1 0 0) +(3.2 0 0) +(3.3 0 0) +(3.4 0 0) +(3.5 0 0) +(3.6 0 0) +(3.7 0 0) +(3.8 0 0) +(3.9 0 0) +(4 0 0) +(0 0.009600014954 0) +(0.1 0.009600014954 0) +(0.2 0.009600014954 0) +(0.3 0.009600014954 0) +(0.4 0.009600014954 0) +(0.5 0.009600014954 0) +(0.6 0.009600014954 0) +(0.7 0.009600014954 0) +(0.8 0.009600014954 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1.968114097 2) +(3.4 1.968114097 2) +(3.5 1.968114097 2) +(3.6 1.968114097 2) +(3.7 1.968114097 2) +(3.8 1.968114097 2) +(3.9 1.968114097 2) +(4 1.968114097 2) +(0 1.979808232 2) +(0.1 1.979808232 2) +(0.2 1.979808232 2) +(0.3 1.979808232 2) +(0.4 1.979808232 2) +(0.5 1.979808232 2) +(0.6 1.979808232 2) +(0.7 1.979808232 2) +(0.8 1.979808232 2) +(0.9 1.979808232 2) +(1 1.979808232 2) +(1.1 1.979808232 2) +(1.2 1.979808232 2) +(1.3 1.979808232 2) +(1.4 1.979808232 2) +(1.5 1.979808232 2) +(1.6 1.979808232 2) +(1.7 1.979808232 2) +(1.8 1.979808232 2) +(1.9 1.979808232 2) +(2 1.979808232 2) +(2.1 1.979808232 2) +(2.2 1.979808232 2) +(2.3 1.979808232 2) +(2.4 1.979808232 2) +(2.5 1.979808232 2) +(2.6 1.979808232 2) +(2.7 1.979808232 2) +(2.8 1.979808232 2) +(2.9 1.979808232 2) +(3 1.979808232 2) +(3.1 1.979808232 2) +(3.2 1.979808232 2) +(3.3 1.979808232 2) +(3.4 1.979808232 2) +(3.5 1.979808232 2) +(3.6 1.979808232 2) +(3.7 1.979808232 2) +(3.8 1.979808232 2) +(3.9 1.979808232 2) +(4 1.979808232 2) +(0 1.990402377 2) +(0.1 1.990402377 2) +(0.2 1.990402377 2) +(0.3 1.990402377 2) +(0.4 1.990402377 2) +(0.5 1.990402377 2) +(0.6 1.990402377 2) +(0.7 1.990402377 2) +(0.8 1.990402377 2) +(0.9 1.990402377 2) +(1 1.990402377 2) +(1.1 1.990402377 2) +(1.2 1.990402377 2) +(1.3 1.990402377 2) +(1.4 1.990402377 2) +(1.5 1.990402377 2) +(1.6 1.990402377 2) +(1.7 1.990402377 2) +(1.8 1.990402377 2) +(1.9 1.990402377 2) +(2 1.990402377 2) +(2.1 1.990402377 2) +(2.2 1.990402377 2) +(2.3 1.990402377 2) +(2.4 1.990402377 2) +(2.5 1.990402377 2) +(2.6 1.990402377 2) +(2.7 1.990402377 2) +(2.8 1.990402377 2) +(2.9 1.990402377 2) +(3 1.990402377 2) +(3.1 1.990402377 2) +(3.2 1.990402377 2) +(3.3 1.990402377 2) +(3.4 1.990402377 2) +(3.5 1.990402377 2) +(3.6 1.990402377 2) +(3.7 1.990402377 2) +(3.8 1.990402377 2) +(3.9 1.990402377 2) +(4 1.990402377 2) +(0 2 2) +(0.1 2 2) +(0.2 2 2) +(0.3 2 2) +(0.4 2 2) +(0.5 2 2) +(0.6 2 2) +(0.7 2 2) +(0.8 2 2) +(0.9 2 2) +(1 2 2) +(1.1 2 2) +(1.2 2 2) +(1.3 2 2) +(1.4 2 2) +(1.5 2 2) +(1.6 2 2) +(1.7 2 2) +(1.8 2 2) +(1.9 2 2) +(2 2 2) +(2.1 2 2) +(2.2 2 2) +(2.3 2 2) +(2.4 2 2) +(2.5 2 2) +(2.6 2 2) +(2.7 2 2) +(2.8 2 2) +(2.9 2 2) +(3 2 2) +(3.1 2 2) +(3.2 2 2) +(3.3 2 2) +(3.4 2 2) +(3.5 2 2) +(3.6 2 2) +(3.7 2 2) +(3.8 2 2) +(3.9 2 2) +(4 2 2) +) + + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/constant/postChannelDict b/ML_LES/ML_LES/ML_LES_Case/constant/postChannelDict new file mode 100755 index 0000000..e1a5d5c --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/constant/postChannelDict @@ -0,0 +1,29 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object postChannelDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +// Seed patches to start layering from +patches ( bottomWall ); + +// Direction in which the layers are +component y; + +// Is the mesh symmetric? If so average(symmetric fields) or +// subtract(asymmetric) contributions from both halves +symmetric true; + + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/constant/transportProperties b/ML_LES/ML_LES/ML_LES_Case/constant/transportProperties new file mode 100755 index 0000000..2f0f1ec --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/constant/transportProperties @@ -0,0 +1,24 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object transportProperties; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +Ubar [0 1 -1 0 0 0 0] (0.1335 0 0); + +transportModel Newtonian; + +nu [0 2 -1 0 0 0 0] 2e-05; + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/constant/turbulenceProperties b/ML_LES/ML_LES/ML_LES_Case/constant/turbulenceProperties new file mode 100755 index 0000000..195c406 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/constant/turbulenceProperties @@ -0,0 +1,100 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object turbulenceProperties; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +simulationType LES; + +LES +{ + LESModel ML_LES; + + turbulence on; + + printCoeffs on; + + delta cubeRootVol; + + filter simple; + + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + dynamicSmagorinskyCoeffs + { + filter simple; + } + + PrandtlCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } + + Cdelta 0.158; + } + + vanDriestCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } + + Aplus 26; + Cdelta 0.158; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/system/blockMeshDict b/ML_LES/ML_LES/ML_LES_Case/system/blockMeshDict new file mode 100755 index 0000000..26ae6ee --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/system/blockMeshDict @@ -0,0 +1,115 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + object blockMeshDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +convertToMeters 1; + +vertices +( + (0 0 0) + (4 0 0) + (0 1 0) + (4 1 0) + (0 2 0) + (4 2 0) + (0 0 2) + (4 0 2) + (0 1 2) + (4 1 2) + (0 2 2) + (4 2 2) +); + +blocks +( + hex (0 1 3 2 6 7 9 8) (40 25 30) simpleGrading (1 10.7028 1) + hex (2 3 5 4 8 9 11 10) (40 25 30) simpleGrading (1 0.0934 1) +); + +edges +( +); + +boundary +( + bottomWall + { + type wall; + faces ((0 1 7 6)); + } + topWall + { + type wall; + faces ((4 10 11 5)); + } + + sides1_half0 + { + type cyclic; + neighbourPatch sides1_half1; + faces ((0 2 3 1)); + } + sides1_half1 + { + type cyclic; + neighbourPatch sides1_half0; + faces ((6 7 9 8)); + } + + sides2_half0 + { + type cyclic; + neighbourPatch sides2_half1; + faces ((2 4 5 3)); + } + sides2_half1 + { + type cyclic; + neighbourPatch sides2_half0; + faces ((8 9 11 10)); + } + + inout1_half0 + { + type cyclic; + neighbourPatch inout1_half1; + faces ((1 3 9 7)); + } + inout1_half1 + { + type cyclic; + neighbourPatch inout1_half0; + faces ((0 6 8 2)); + } + + inout2_half0 + { + type cyclic; + neighbourPatch inout2_half1; + faces ((3 5 11 9)); + } + inout2_half1 + { + type cyclic; + neighbourPatch inout2_half0; + faces ((2 8 10 4)); + } +); + +mergePatchPairs +( +); + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/system/controlDict b/ML_LES/ML_LES/ML_LES_Case/system/controlDict new file mode 100755 index 0000000..2daaa1c --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/system/controlDict @@ -0,0 +1,80 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object controlDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +libs +( +"ML_LES.so" +); + +application pimpleFoam; + +startFrom startTime; + +startTime 0; + +stopAt endTime; + +endTime 1000; + +deltaT 0.2; + +writeControl timeStep; + +writeInterval 200; + +purgeWrite 0; + +writeFormat ascii; + +writePrecision 6; + +writeCompression off; + +timeFormat general; + +timePrecision 6; + +runTimeModifiable true; + +functions +{ + fieldAverage1 + { + type fieldAverage; + libs ("libfieldFunctionObjects.so"); + writeControl writeTime; + + fields + ( + U + { + mean on; + prime2Mean on; + base time; + } + + p + { + mean on; + prime2Mean on; + base time; + } + ); + } +} + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/system/decomposeParDict b/ML_LES/ML_LES/ML_LES_Case/system/decomposeParDict new file mode 100755 index 0000000..b469469 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/system/decomposeParDict @@ -0,0 +1,135 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + note "mesh decomposition control dictionary"; + object decomposeParDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +numberOfSubdomains 4; + +//- Keep owner and neighbour on same processor for faces in zones: +// preserveFaceZones (heater solid1 solid3); + +//- Keep owner and neighbour on same processor for faces in patches: +// (makes sense only for cyclic patches) +//preservePatches (cyclic_half0 cyclic_half1); + +//- Keep all of faceSet on a single processor. This puts all cells +// connected with a point, edge or face on the same processor. +// (just having face connected cells might not guarantee a balanced +// decomposition) +// The processor can be -1 (the decompositionMethod chooses the processor +// for a good load balance) or explicitly provided (upsets balance). +//singleProcessorFaceSets ((f0 -1)); + + +//- Use the volScalarField named here as a weight for each cell in the +// decomposition. For example, use a particle population field to decompose +// for a balanced number of particles in a lagrangian simulation. +// weightField dsmcRhoNMean; + +// method scotch; +// method hierarchical; +method simple; +// method metis; +// method manual; +// method multiLevel; +// method structured; // does 2D decomposition of structured mesh + +multiLevelCoeffs +{ + // Decomposition methods to apply in turn. This is like hierarchical but + // fully general - every method can be used at every level. + + level0 + { + numberOfSubdomains 64; + //method simple; + //simpleCoeffs + //{ + // n (2 1 1); + // delta 0.001; + //} + method scotch; + } + level1 + { + numberOfSubdomains 4; + method scotch; + } +} + +// Desired output + +simpleCoeffs +{ + n (1 2 2); + delta 0.001; +} + +hierarchicalCoeffs +{ + n (1 2 1); + delta 0.001; + order xyz; +} + +metisCoeffs +{ + /* + processorWeights + ( + 1 + 1 + 1 + 1 + ); + */ +} + +scotchCoeffs +{ + //processorWeights + //( + // 1 + // 1 + // 1 + // 1 + //); + //writeGraph true; + //strategy "b"; +} + +manualCoeffs +{ + dataFile "decompositionData"; +} + +structuredCoeffs +{ + // Patches to do 2D decomposition on. Structured mesh only; cells have + // to be in 'columns' on top of patches. + patches (bottomPatch); +} + +//// Is the case distributed? Note: command-line argument -roots takes +//// precedence +//distributed yes; +//// Per slave (so nProcs-1 entries) the directory above the case. +//roots +//( +// "/tmp" +// "/tmp" +//); + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/system/fvSchemes b/ML_LES/ML_LES/ML_LES_Case/system/fvSchemes new file mode 100755 index 0000000..78fc3ba --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/system/fvSchemes @@ -0,0 +1,60 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object fvSchemes; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +ddtSchemes +{ + default backward; +} + +gradSchemes +{ + default Gauss linear; +} + +divSchemes +{ + default none; + div(phi,U) Gauss linear; + div(phi,k) Gauss limitedLinear 1; + div(phi,B) Gauss limitedLinear 1; + div(B) Gauss linear; + div(phi,nuTilda) Gauss limitedLinear 1; + div((nuEff*dev2(T(grad(U))))) Gauss linear; +} + +laplacianSchemes +{ + default Gauss linear corrected; +} + +interpolationSchemes +{ + default linear; +} + +snGradSchemes +{ + default corrected; +} + +wallDist +{ + method meshWave; +} + + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Case/system/fvSolution b/ML_LES/ML_LES/ML_LES_Case/system/fvSolution new file mode 100755 index 0000000..348f3e5 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Case/system/fvSolution @@ -0,0 +1,62 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object fvSolution; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +solvers +{ + p + { + solver GAMG; + tolerance 0; + relTol 0.1; + smoother GaussSeidel; + } + + pFinal + { + $p; + smoother DICGaussSeidel; + tolerance 1e-06; + relTol 0; + } + + "(U|k|nuTilda)" + { + solver smoothSolver; + smoother symGaussSeidel; + tolerance 1e-05; + relTol 0.1; + } + + "(U|k|nuTilda)Final" + { + $U; + tolerance 1e-05; + relTol 0; + } +} + +PIMPLE +{ + nOuterCorrectors 1; + nCorrectors 2; + nNonOrthogonalCorrectors 0; + pRefCell 1001; + pRefValue 0; +} + + +// ************************************************************************* // diff --git a/ML_LES/ML_LES/ML_LES_Model/ML_LES.C b/ML_LES/ML_LES/ML_LES_Model/ML_LES.C new file mode 100755 index 0000000..a326f11 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/ML_LES.C @@ -0,0 +1,300 @@ +/*---------------------------------------------------------------------------*\ + ========= | + \\ / F ield | OpenFOAM: The Open Source CFD Toolbox + \\ / O peration | + \\ / A nd | Copyright (C) 2011-2015 OpenFOAM Foundation + \\/ M anipulation | +------------------------------------------------------------------------------- +License + This file is part of OpenFOAM. + + OpenFOAM is free software: you can redistribute it and/or modify it + under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + OpenFOAM is distributed in the hope that it will be useful, but WITHOUT + ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or + FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + for more details. + + You should have received a copy of the GNU General Public License + along with OpenFOAM. If not, see . + +\*---------------------------------------------------------------------------*/ + +#include "ML_LES.H" +#include "fvOptions.H" +#include "wallDist.H" +#include "scope_guard.hpp" + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +namespace Foam +{ +namespace LESModels +{ + +// * * * * * * * * * * * * * Private Member Functions * * * * * * * * * * * // + +template +void ML_LES::correctNut +( + const tmp& gradU +) +{ + this->nut_ = max(Cs_*sqr(this->delta())*mag(dev(symm(gradU))),-1.0*this->nu()); + this->nut_.correctBoundaryConditions(); + fv::options::New(this->mesh_).correct(this->nut_); + + BasicTurbulenceModel::correctNut(); +} + + +template +void ML_LES::correctNut() +{ + correctNut(fvc::grad(this->U_)); +} + + +// * * * * * * * * * * * * * * * * Constructors * * * * * * * * * * * * * * // + +template +ML_LES::ML_LES +( + const alphaField& alpha, + const rhoField& rho, + const volVectorField& U, + const surfaceScalarField& alphaRhoPhi, + const surfaceScalarField& phi, + const transportModel& transport, + const word& propertiesName, + const word& type +) +: + LESeddyViscosity + ( + type, + alpha, + rho, + U, + alphaRhoPhi, + phi, + transport, + propertiesName + ), + + k_ + ( + IOobject + ( + IOobject::groupName("k", this->U_.group()), + this->runTime_.timeName(), + this->mesh_, + IOobject::MUST_READ, + IOobject::AUTO_WRITE + ), + this->mesh_ + ), + + Cs_ + ( + IOobject + ( + IOobject::groupName("Cs", this->U_.group()), + this->runTime_.timeName(), + this->mesh_, + IOobject::READ_IF_PRESENT, + IOobject::AUTO_WRITE + ), + this->mesh_, + dimensionedScalar ("Cs", dimless,SMALL) + ), + simpleFilter_(U.mesh()), + filterPtr_(LESfilter::New(U.mesh(), this->coeffDict())), + filter_(filterPtr_()), + y_(wallDist::New(this->mesh_).y()), + // Follow convention for graph operations - append '_ph' for placeholder, simply '_' for output, '_op' for an operation like backprop + // Constructor loads a frozen *.pb graph from the path + graph_ + ( + tf_utils::LoadGraph("./ML_LES.pb") + ), + + // Defines an input operation - this must correspond to the name specified in python API + input_ph_ + ( + {TF_GraphOperationByName(graph_, "input_placeholder"), 0} // the operation would look like "input_placeholder:0" in model.inputs[0] (keras) + + ), + // Defines an output operation - this must correspond to the name specified in python API + output_ + ( + {TF_GraphOperationByName(graph_, "output_value/BiasAdd"), 0} // the operation would look like "output_value/BiasAdd:0" in model.outputs[0] (keras) + ) +{ +// bound(k_, this->kMin_); + + if (type == typeName) + { + this->printCoeffs(type); + } +} + +// * * * * * * * * * * * * * * * Member Functions * * * * * * * * * * * * * // + +template +bool ML_LES::read() +{ + if (LESeddyViscosity::read()) + { + filter_.read(this->coeffDict()); + + return true; + } + else + { + return false; + } +} + + +template +void ML_LES::correct() +{ + if (!this->turbulence_) + { + return; + } + + // Local references + // const surfaceScalarField& phi = this->phi_; + // const volVectorField& U = this->U_; + // fv::options& fvOptions(fv::options::New(this->mesh_)); // Source term + + LESeddyViscosity::correct(); // For changing mesh - coming from turbulenceModel.C through virtual shit + + int num_inputs = 9; + int num_outputs = 1; + float mean_array [num_inputs+num_outputs] = {4.948654692193851069e-05,-1.416845935576197153e-03,1.695804398322601982e-04,-4.909234209068177434e-05,7.200956380997814788e-04,-3.949331152012949186e-07,1.155548212380012041e-01,-1.447936297672789625e-05,-1.249577196433397854e-05,4.991843687885162174e-03}; + float std_array [num_inputs+num_outputs] = {5.156828074413144503e-02,4.477068164228664160e-01,7.504360316118742491e-02,5.840131322144209713e-02,8.381264536219842909e-02,5.977095870302145258e-02,3.844051667887211921e-02,5.081242374826853286e-03,7.321494585983414141e-03,1.157813043774712919e-02}; + + run_ml_graph(mean_array,std_array,num_inputs,num_outputs); +} + +template +void ML_LES::run_ml_graph(float* mean_array, float* std_array, int num_inputs, int num_outputs) +{ + // omp_set_num_threads(omp_num_threads_); + tmp tgradU(fvc::grad(this->U_)); + const volTensorField& gradU = tgradU(); + + // Structure for tensors in ML + int num_cells = this->mesh_.cells().size(); + + volSymmTensorField S(dev(symm(gradU))); + volScalarField s11 = S.component(tensor::XX); + volScalarField s12 = S.component(tensor::XY); + volScalarField s13 = S.component(tensor::XZ); + volScalarField s22 = S.component(tensor::YY); + volScalarField s23 = S.component(tensor::YZ); + volScalarField s33 = S.component(tensor::ZZ); + + + volScalarField u_ = this->U_.component(vector::X); + volScalarField v_ = this->U_.component(vector::Y); + volScalarField w_ = this->U_.component(vector::Z); + + volScalarField filter_width(this->delta()); + + // Some tensorflow pointer requirements + auto status_ = TF_NewStatus(); + SCOPE_EXIT{ TF_DeleteStatus(status_); }; // Auto-delete on scope exit. + + auto options_ = TF_NewSessionOptions(); + SCOPE_EXIT{ TF_DeleteSessionOptions(options_); }; // Auto-delete on scope exit. + + auto sess_ = TF_NewSession(graph_, options_, status_); + SCOPE_EXIT{ tf_utils::DeleteSession(sess_); }; // Auto-delete on scope exit. + + { + + float input_vals[num_cells][num_inputs]; + const std::vector input_dims = {num_cells, num_inputs}; + + // #pragma omp parallel for + forAll(s11.internalField(), id) // for boundary field use u_.boundaryField() + { + + float i1 = (s11[id] - mean_array[0])/(std_array[0]); + float i2 = (s12[id] - mean_array[1])/(std_array[1]); + float i3 = (s13[id] - mean_array[2])/(std_array[2]); + float i4 = (s22[id] - mean_array[3])/(std_array[3]); + float i5 = (s23[id] - mean_array[4])/(std_array[4]); + float i6 = (s33[id] - mean_array[5])/(std_array[5]); + float i7 = (u_[id] - mean_array[6])/(std_array[6]); + float i8 = (v_[id] - mean_array[7])/(std_array[7]); + float i9 = (w_[id] - mean_array[8])/(std_array[8]); + + // float i7 = (filter_width[id] - mean_array[6])/(std_array[6]); + // float i8 = (y_[id] - mean_array[7])/(std_array[7]); + + input_vals[id][0] = i1; + input_vals[id][1] = i2; + input_vals[id][2] = i3; + input_vals[id][3] = i4; + input_vals[id][4] = i5; + input_vals[id][5] = i6; + input_vals[id][6] = i7; + input_vals[id][7] = i8; + input_vals[id][8] = i9; + } + + // Set up TF C API stuff + TF_Tensor* output_tensor_ = nullptr; + TF_Tensor* input_tensor_ = tf_utils::CreateTensor(TF_FLOAT, + input_dims.data(), input_dims.size(), + &input_vals, num_cells*num_inputs*sizeof(float)); + + SCOPE_EXIT{ tf_utils::DeleteTensor(input_tensor_); }; // Auto-delete on scope exit. + SCOPE_EXIT{ tf_utils::DeleteTensor(output_tensor_); }; // Auto-delete on scope exit. + + // // Arrays of tensors + // TF_Tensor* input_tensors_[1] = {input_tensor_}; + // TF_Tensor* output_tensors_[1] = {output_tensor_}; + // // Arrays of operations + // TF_Output inputs[1] = {input_ph_}; + // TF_Output outputs[1] = {output_}; + + TF_SessionRun(sess_, + nullptr, // Run options. + &input_ph_, &input_tensor_, 1, // Input tensor ops, input tensor values, number of inputs. + &output_, &output_tensor_, 1, // Output tensor ops, output tensor values, number of outputs. + nullptr, 0, // Target operations, number of targets. + nullptr, // Run metadata. + status_ // Output status. + ); + + const auto data = static_cast(TF_TensorData(output_tensor_)); + for (int i = 0; i < num_cells; i++) + { + this->Cs_[i] = data[num_outputs*i]*std_array[num_inputs] + mean_array[num_inputs]; // Funnel changes back into OF - row major order + } + + this->Cs_ = filter_(this->Cs_); + correctNut(gradU); + // printf("%s\n", "We are using TF for Smagorinsky coefficient prediction"); + // this->nut_.correctBoundaryConditions(); + // fv::options::New(this->mesh_).correct(this->nut_); + + } // Scope for deleting local variable input_vals +} + + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +} // End namespace LESModels +} // End namespace Foam + +// ************************************************************************* // \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/ML_LES.H b/ML_LES/ML_LES/ML_LES_Model/ML_LES.H new file mode 100755 index 0000000..b968d9e --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/ML_LES.H @@ -0,0 +1,175 @@ +/*---------------------------------------------------------------------------*\ + ========= | + \\ / F ield | OpenFOAM: The Open Source CFD Toolbox + \\ / O peration | + \\ / A nd | Copyright (C) 2011-2016 OpenFOAM Foundation + \\/ M anipulation | +------------------------------------------------------------------------------- +License + This file is part of OpenFOAM. + + OpenFOAM is free software: you can redistribute it and/or modify it + under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + OpenFOAM is distributed in the hope that it will be useful, but WITHOUT + ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or + FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + for more details. + + You should have received a copy of the GNU General Public License + along with OpenFOAM. If not, see . + +Class + Foam::LESModels::ML_LES + +Group + grpLESTurbulence + +Description + Dynamic SGS model with local averaging, based on Germano and Lilly works. + + An older version of this code had been developed for version 2.3.1 and older by Alberto Passalacqua. + + The current version has been developed for version 4.1 + + Ehsan Asgari, e.asgari@aut.ac.ir + +SourceFiles + ML_LES.C + +\*---------------------------------------------------------------------------*/ + +#ifndef ML_LES_H +#define ML_LES_H + +#include +#include "LESeddyViscosity.H" +#include "simpleFilter.H" +#include "tf_utils.H" // Loads in tensorflow functionality + + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +namespace Foam +{ +namespace LESModels +{ + +/*---------------------------------------------------------------------------*\ + Class ML_LES Declaration +\*---------------------------------------------------------------------------*/ + +template +class ML_LES +: + public LESeddyViscosity +{ + // Private Member Functions + + // Disallow default bitwise copy construct and assignment + ML_LES(const ML_LES&); + void operator=(const ML_LES&); + + +protected: + + // Protected data + volScalarField k_; + volScalarField Cs_; + +// volScalarField beta_; + simpleFilter simpleFilter_; + autoPtr filterPtr_; + LESfilter& filter_; + volScalarField y_; + + // Tensorflow graph operations - protected members of this class - top three not used in constructor definition since they don't have default values + TF_Graph* graph_; + TF_Output input_ph_; + TF_Output output_; + + + // Protected Member Functions + + //- Update sub-grid eddy-viscosity + void correctNut(const tmp& gradU); + + virtual void correctNut(); + + void run_ml_graph(float* mean_array, float* std_array, int num_inputs, int num_outputs); + + +public: + + typedef typename BasicTurbulenceModel::alphaField alphaField; + typedef typename BasicTurbulenceModel::rhoField rhoField; + typedef typename BasicTurbulenceModel::transportModel transportModel; + + //- Runtime type information + TypeName("ML_LES"); + + + // Constructors + + //- Construct from components + ML_LES + ( + const alphaField& alpha, + const rhoField& rho, + const volVectorField& U, + const surfaceScalarField& alphaRhoPhi, + const surfaceScalarField& phi, + const transportModel& transport, + const word& propertiesName = turbulenceModel::propertiesName, + const word& type = typeName + ); + + + //- Destructor + virtual ~ML_LES() + {} + + + // Member Functions + + //- Read model coefficients if they have changed + virtual bool read(); + + //- Return SGS kinetic energy + virtual tmp k() const + { + return k_; + } + + //- Return the effective diffusivity for k + tmp DkEff() const + { + return tmp + ( + new volScalarField("DkEff", this->nut_ + this->nu()) + ); + } + + //- Correct Eddy-Viscosity and related properties + virtual void correct(); +}; + + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +} // End namespace LESModels +} // End namespace Foam + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +#ifdef NoRepository + #include "ML_LES.C" +#endif + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +#endif + +// ************************************************************************* // \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/files b/ML_LES/ML_LES/ML_LES_Model/Make/files new file mode 100755 index 0000000..6a09305 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/Make/files @@ -0,0 +1,4 @@ +maketurbulentTransportModels.C +tf_utils.C + +LIB = $(FOAM_USER_LIBBIN)/ML_LES diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.C.dep b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.C.dep new file mode 100755 index 0000000..1ab034d --- /dev/null +++ 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+$(WM_PROJECT_DIR)/src/finiteVolume/lnInclude/zeroGradientFvPatchFields.H \ +$(WM_PROJECT_DIR)/src/finiteVolume/lnInclude/zeroGradientFvPatchField.H \ +$(WM_PROJECT_DIR)/src/finiteVolume/lnInclude/zeroGradientFvPatchField.C \ +$(WM_PROJECT_DIR)/src/finiteVolume/lnInclude/patchDistMethodTemplates.C \ +scope_guard.hpp \ + + diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o new file mode 100755 index 0000000..5ed6b74 Binary files /dev/null and b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o differ diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/options b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/options new file mode 100755 index 0000000..3122c35 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/options @@ -0,0 +1,65 @@ +# 1 "Make/options" +# 1 "" +# 1 "" +# 8 "" +# 1 "/usr/include/stdc-predef.h" 1 3 4 + +# 17 "/usr/include/stdc-predef.h" 3 4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +# 8 "" 2 +# 1 "Make/options" +ptFLAGS += -fopenmp + +EXE_INC = -std=c++0x -I$(LIB_SRC)/TurbulenceModels/turbulenceModels/lnInclude -I$(LIB_SRC)/TurbulenceModels/incompressible/lnInclude -I$(LIB_SRC)/transportModels -I$(LIB_SRC)/finiteVolume/lnInclude -I$(LIB_SRC)/meshTools/lnInclude + + + + + + +LIB_LIBS = -lincompressibleTransportModels -lturbulenceModels -lfiniteVolume -lmeshTools -ltensorflow -fopenmp diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/sourceFiles b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/sourceFiles new file mode 100755 index 0000000..1da348b --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/sourceFiles @@ -0,0 +1,3 @@ +SOURCE = \ +maketurbulentTransportModels.C \ +tf_utils.C diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep new file mode 100755 index 0000000..15f8289 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep @@ -0,0 +1,5 @@ +$(OBJECTS_DIR)/tf_utils.C.dep: \ +tf_utils.C \ +tf_utils.H \ + + diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/tf_utils.o b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/tf_utils.o new file mode 100755 index 0000000..65532fc Binary files /dev/null and b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/tf_utils.o differ diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/variables b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/variables new file mode 100755 index 0000000..c8709e9 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/Make/linux64GccDPInt64Opt/variables @@ -0,0 +1 @@ +LIB = $(FOAM_USER_LIBBIN)/ML_LES diff --git a/ML_LES/ML_LES/ML_LES_Model/Make/options b/ML_LES/ML_LES/ML_LES_Model/Make/options new file mode 100755 index 0000000..81128da --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/Make/options @@ -0,0 +1,16 @@ +ptFLAGS += -fopenmp + +EXE_INC = -std=c++0x \ + -I$(LIB_SRC)/TurbulenceModels/turbulenceModels/lnInclude \ + -I$(LIB_SRC)/TurbulenceModels/incompressible/lnInclude \ + -I$(LIB_SRC)/transportModels \ + -I$(LIB_SRC)/finiteVolume/lnInclude \ + -I$(LIB_SRC)/meshTools/lnInclude \ + +LIB_LIBS = \ + -lincompressibleTransportModels \ + -lturbulenceModels \ + -lfiniteVolume \ + -lmeshTools \ + -ltensorflow \ + -fopenmp diff --git a/ML_LES/ML_LES/ML_LES_Model/lnInclude/ML_LES.C b/ML_LES/ML_LES/ML_LES_Model/lnInclude/ML_LES.C new file mode 120000 index 0000000..ae28ee4 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/lnInclude/ML_LES.C @@ -0,0 +1 @@ +../ML_LES.C \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/lnInclude/ML_LES.H b/ML_LES/ML_LES/ML_LES_Model/lnInclude/ML_LES.H new file mode 120000 index 0000000..fbb1e8b --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/lnInclude/ML_LES.H @@ -0,0 +1 @@ +../ML_LES.H \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/lnInclude/maketurbulentTransportModels.C b/ML_LES/ML_LES/ML_LES_Model/lnInclude/maketurbulentTransportModels.C new file mode 120000 index 0000000..0b8de36 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/lnInclude/maketurbulentTransportModels.C @@ -0,0 +1 @@ +../maketurbulentTransportModels.C \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/lnInclude/scope_guard.hpp b/ML_LES/ML_LES/ML_LES_Model/lnInclude/scope_guard.hpp new file mode 120000 index 0000000..f2b14de --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/lnInclude/scope_guard.hpp @@ -0,0 +1 @@ +../scope_guard.hpp \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/lnInclude/tf_utils.C b/ML_LES/ML_LES/ML_LES_Model/lnInclude/tf_utils.C new file mode 120000 index 0000000..28a9278 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/lnInclude/tf_utils.C @@ -0,0 +1 @@ +../tf_utils.C \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/lnInclude/tf_utils.H b/ML_LES/ML_LES/ML_LES_Model/lnInclude/tf_utils.H new file mode 120000 index 0000000..b478e26 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/lnInclude/tf_utils.H @@ -0,0 +1 @@ +../tf_utils.H \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/maketurbulentTransportModels.C b/ML_LES/ML_LES/ML_LES_Model/maketurbulentTransportModels.C new file mode 100755 index 0000000..cd9b5a0 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/maketurbulentTransportModels.C @@ -0,0 +1,29 @@ +/*---------------------------------------------------------------------------*\ + ========= | + \\ / F ield | OpenFOAM: The Open Source CFD Toolbox + \\ / O peration | + \\ / A nd | Copyright (C) 2013-2016 OpenFOAM Foundation + \\/ M anipulation | +------------------------------------------------------------------------------- +License + This file is part of OpenFOAM. + + OpenFOAM is free software: you can redistribute it and/or modify it + under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + OpenFOAM is distributed in the hope that it will be useful, but WITHOUT + ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or + FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + for more details. + + You should have received a copy of the GNU General Public License + along with OpenFOAM. If not, see . + +\*---------------------------------------------------------------------------*/ + +#include "turbulentTransportModels.H" +#include "simpleFilter.H" +#include "ML_LES.H" +makeLESModel(ML_LES); \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/scope_guard.hpp b/ML_LES/ML_LES/ML_LES_Model/scope_guard.hpp new file mode 100755 index 0000000..00f85de --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/scope_guard.hpp @@ -0,0 +1,335 @@ +// _____ _____ _ _____ +// / ____| / ____| | | / ____|_ _ +// | (___ ___ ___ _ __ ___ | | __ _ _ __ _ _ __ __| | | | _| |_ _| |_ +// \___ \ / __/ _ \| '_ \ / _ \ | | |_ | | | |/ _` | '__/ _` | | | |_ _|_ _| +// ____) | (_| (_) | |_) | __/ | |__| | |_| | (_| | | | (_| | | |____|_| |_| +// |_____/ \___\___/| .__/ \___| \_____|\__,_|\__,_|_| \__,_| \_____| +// | | https://github.com/Neargye/scope_guard +// |_| version 0.6.0 +// +// Licensed under the MIT License . +// SPDX-License-Identifier: MIT +// Copyright (c) 2018 - 2019 Daniil Goncharov . +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in all +// copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +// SOFTWARE. + +#ifndef NEARGYE_SCOPE_GUARD_HPP +#define NEARGYE_SCOPE_GUARD_HPP + +#include +#include +#include +#include +#if (defined(_MSC_VER) && _MSC_VER >= 1900) || ((defined(__clang__) || defined(__GNUC__)) && __cplusplus >= 201700L) +#include +#endif + +// scope_guard throwable settings: +// SCOPE_GUARD_NO_THROW_CONSTRUCTIBLE requires nothrow constructible action. +// SCOPE_GUARD_MAY_THROW_ACTION action may throw exceptions. +// SCOPE_GUARD_NO_THROW_ACTION requires noexcept action. +// SCOPE_GUARD_SUPPRESS_THROW_ACTIONS exceptions during action will be suppressed. + +#if !defined(SCOPE_GUARD_MAY_THROW_ACTION) && !defined(SCOPE_GUARD_NO_THROW_ACTION) && !defined(SCOPE_GUARD_SUPPRESS_THROW_ACTIONS) +# define SCOPE_GUARD_MAY_THROW_ACTION +#elif (defined(SCOPE_GUARD_MAY_THROW_ACTION) + defined(SCOPE_GUARD_NO_THROW_ACTION) + defined(SCOPE_GUARD_SUPPRESS_THROW_ACTIONS)) > 1 +# error Only one of SCOPE_GUARD_MAY_THROW_ACTION and SCOPE_GUARD_NO_THROW_ACTION and SCOPE_GUARD_SUPPRESS_THROW_ACTIONS may be defined. +#endif + +#if defined(SCOPE_GUARD_NO_THROW_ACTION) +# define __SCOPE_GUARD_ACTION_NOEXCEPT noexcept +#else +# define __SCOPE_GUARD_ACTION_NOEXCEPT +#endif + +#if defined(SCOPE_GUARD_SUPPRESS_THROW_ACTIONS) && (defined(__cpp_exceptions) || defined(__EXCEPTIONS) || defined(_CPPUNWIND)) +# define __SCOPE_GUARD_NOEXCEPT(...) noexcept +# define __SCOPE_GUARD_TRY try { +# define __SCOPE_GUARD_CATCH } catch (...) {} +#else +# define __SCOPE_GUARD_NOEXCEPT(...) noexcept(__VA_ARGS__) +# define __SCOPE_GUARD_TRY +# define __SCOPE_GUARD_CATCH +#endif + +namespace scope_guard { + +namespace detail { + +#if defined(_MSC_VER) && _MSC_VER < 1900 +inline int uncaught_exceptions() noexcept { + return *(reinterpret_cast(static_cast(static_cast(_getptd())) + (sizeof(void*) == 8 ? 0x100 : 0x90))); +} +#elif (defined(__clang__) || defined(__GNUC__)) && __cplusplus < 201700L +struct __cxa_eh_globals; +extern "C" __cxa_eh_globals* __cxa_get_globals() noexcept; +inline int uncaught_exceptions() noexcept { + return *(reinterpret_cast(static_cast(static_cast(__cxa_get_globals())) + sizeof(void*))); +} +#else +inline int uncaught_exceptions() noexcept { + return std::uncaught_exceptions(); +} +#endif + +class on_exit_policy { + bool execute_; + + public: + explicit on_exit_policy(bool execute) noexcept : execute_{execute} {} + + void dismiss() noexcept { + execute_ = false; + } + + bool should_execute() const noexcept { + return execute_; + } +}; + +class on_fail_policy { + int ec_; + + public: + explicit on_fail_policy(bool execute) noexcept : ec_{execute ? uncaught_exceptions() : -1} {} + + void dismiss() noexcept { + ec_ = -1; + } + + bool should_execute() const noexcept { + return ec_ != -1 && ec_ < uncaught_exceptions(); + } +}; + +class on_success_policy { + int ec_; + + public: + explicit on_success_policy(bool execute) noexcept : ec_{execute ? uncaught_exceptions() : -1} {} + + void dismiss() noexcept { + ec_ = -1; + } + + bool should_execute() const noexcept { + return ec_ != -1 && ec_ >= uncaught_exceptions(); + } +}; + +template +class scope_guard { + using A = typename std::decay::type; + using invoke_action_result_t = decltype((std::declval())()); + using is_nothrow_invocable_action = std::integral_constant())())>; + + static_assert(std::is_same::value, + "scope_guard requires no-argument action, that returns void."); + static_assert(std::is_same::value || std::is_same::value || std::is_same::value, + "scope_guard requires on_exit_policy, on_fail_policy or on_success_policy."); +#if defined(SCOPE_GUARD_NO_THROW_ACTION) + static_assert(is_nothrow_invocable_action::value, + "scope_guard requires noexcept invocable action."); +#endif +#if defined(SCOPE_GUARD_NO_THROW_CONSTRUCTIBLE) + static_assert(std::is_nothrow_move_constructible::value, + "scope_guard requires nothrow constructible action."); +#endif + + P policy_; + A action_; + + void* operator new(std::size_t) = delete; + void operator delete(void*) = delete; + + public: + scope_guard() = delete; + scope_guard(const scope_guard&) = delete; + scope_guard& operator=(const scope_guard&) = delete; + scope_guard& operator=(scope_guard&&) = delete; + + scope_guard(scope_guard&& other) noexcept(std::is_nothrow_move_constructible::value) + : policy_{false}, + action_{std::move(other.action_)} { + policy_ = std::move(other.policy_); + other.policy_.dismiss(); + } + + explicit scope_guard(const A& action) = delete; + explicit scope_guard(A& action) = delete; + + explicit scope_guard(A&& action) noexcept(std::is_nothrow_move_constructible::value) + : policy_{true}, + action_{std::move(action)} {} + + void dismiss() noexcept { + policy_.dismiss(); + } + + ~scope_guard() __SCOPE_GUARD_NOEXCEPT(is_nothrow_invocable_action::value) { + if (policy_.should_execute()) { + __SCOPE_GUARD_TRY + action_(); + __SCOPE_GUARD_CATCH + } + } +}; + +} // namespace scope_guard::detail + +template +using scope_exit = detail::scope_guard; + +template +using scope_fail = detail::scope_guard; + +template +using scope_succes = detail::scope_guard; + +// ATTR_NODISCARD encourages the compiler to issue a warning if the return value is discarded. +#if !defined(ATTR_NODISCARD) +# if defined(__clang__) +# if (__clang_major__ * 10 + __clang_minor__) >= 39 && __cplusplus >= 201703L +# define ATTR_NODISCARD [[nodiscard]] +# else +# define ATTR_NODISCARD __attribute__((__warn_unused_result__)) +# endif +# elif defined(__GNUC__) +# if __GNUC__ >= 7 && __cplusplus >= 201703L +# define ATTR_NODISCARD [[nodiscard]] +# else +# define ATTR_NODISCARD __attribute__((__warn_unused_result__)) +# endif +# elif defined(_MSC_VER) +# if _MSC_VER >= 1911 && defined(_MSVC_LANG) && _MSVC_LANG >= 201703L +# define ATTR_NODISCARD [[nodiscard]] +# elif defined(_Check_return_) +# define ATTR_NODISCARD _Check_return_ +# else +# define ATTR_NODISCARD +# endif +# else +# define ATTR_NODISCARD +# endif +#endif + +template +ATTR_NODISCARD scope_exit make_scope_exit(F&& action) noexcept(noexcept(scope_exit(std::forward(action)))) { + return scope_exit{std::forward(action)}; +} + +template +ATTR_NODISCARD scope_fail make_scope_fail(F&& action) noexcept(noexcept(scope_fail(std::forward(action)))) { + return scope_fail{std::forward(action)}; +} + +template +ATTR_NODISCARD scope_succes make_scope_succes(F&& action) noexcept(noexcept(scope_succes(std::forward(action)))) { + return scope_succes{std::forward(action)}; +} + +namespace detail { + +struct scope_exit_tag {}; + +template +scope_exit operator+(scope_exit_tag, F&& action) noexcept(noexcept(scope_exit(std::forward(action)))) { + return scope_exit{std::forward(action)}; +} + +struct scope_fail_tag {}; + +template +scope_fail operator+(scope_fail_tag, F&& action) noexcept(noexcept(scope_fail(std::forward(action)))) { + return scope_fail{std::forward(action)}; +} + +struct scope_succes_tag {}; + +template +scope_succes operator+(scope_succes_tag, F&& action) noexcept(noexcept(scope_succes(std::forward(action)))) { + return scope_succes{std::forward(action)}; +} + +} // namespace scope_guard::detail + +} // namespace scope_guard + +// ATTR_MAYBE_UNUSED suppresses compiler warnings on unused entities, if any. +#if !defined(ATTR_MAYBE_UNUSED) +# if defined(__clang__) +# if (__clang_major__ * 10 + __clang_minor__) >= 39 && __cplusplus >= 201703L +# define ATTR_MAYBE_UNUSED [[maybe_unused]] +# else +# define ATTR_MAYBE_UNUSED __attribute__((__unused__)) +# endif +# elif defined(__GNUC__) +# if __GNUC__ >= 7 && __cplusplus >= 201703L +# define ATTR_MAYBE_UNUSED [[maybe_unused]] +# else +# define ATTR_MAYBE_UNUSED __attribute__((__unused__)) +# endif +# elif defined(_MSC_VER) +# if _MSC_VER >= 1911 && defined(_MSVC_LANG) && _MSVC_LANG >= 201703L +# define ATTR_MAYBE_UNUSED [[maybe_unused]] +# else +# define ATTR_MAYBE_UNUSED __pragma(warning(suppress : 4100 4101 4189)) +# endif +# else +# define ATTR_MAYBE_UNUSED +# endif +#endif + +#define __SCOPE_GUARD_STR_CONCAT_IMPL(s1, s2) s1##s2 +#define __SCOPE_GUARD_STR_CONCAT(s1, s2) __SCOPE_GUARD_STR_CONCAT_IMPL(s1, s2) + +#if defined(__COUNTER__) +# define __SCOPE_GUARD_COUNTER __COUNTER__ +#elif defined(__LINE__) +# define __SCOPE_GUARD_COUNTER __LINE__ +#endif + +#if __cplusplus >= 201703L || defined(_MSVC_LANG) && _MSVC_LANG >= 201703L +# define __SCOPE_GUARD_WITH(g) if (g; true) +#else +# define __SCOPE_GUARD_WITH_IMPL(g, i) if (int i = 1) for (g; i; --i) +# define __SCOPE_GUARD_WITH(g) __SCOPE_GUARD_WITH_IMPL(g, __SCOPE_GUARD_STR_CONCAT(__scope_guard_with_internal__object_, __SCOPE_GUARD_COUNTER)) +#endif + +// SCOPE_EXIT executing action on scope exit. +#define MAKE_SCOPE_EXIT(name) auto name = ::scope_guard::detail::scope_exit_tag{} + [&]() __SCOPE_GUARD_ACTION_NOEXCEPT -> void +#define SCOPE_EXIT ATTR_MAYBE_UNUSED const MAKE_SCOPE_EXIT(__SCOPE_GUARD_STR_CONCAT(__scope_guard_exit__object_, __SCOPE_GUARD_COUNTER)) +#define WITH_SCOPE_EXIT(guard) __SCOPE_GUARD_WITH(SCOPE_EXIT{guard}) + +// SCOPE_FAIL executing action on scope exit when an exception has been thrown before scope exit. +#define MAKE_SCOPE_FAIL(name) auto name = ::scope_guard::detail::scope_fail_tag{} + [&]() __SCOPE_GUARD_ACTION_NOEXCEPT -> void +#define SCOPE_FAIL ATTR_MAYBE_UNUSED const MAKE_SCOPE_FAIL(__SCOPE_GUARD_STR_CONCAT(__scope_guard_fail__object_, __SCOPE_GUARD_COUNTER)) +#define WITH_SCOPE_FAIL(guard) __SCOPE_GUARD_WITH(SCOPE_FAIL{guard}) + +// SCOPE_SUCCESS executing action on scope exit when no exceptions have been thrown before scope exit. +#define MAKE_SCOPE_SUCCESS(name) auto name = ::scope_guard::detail::scope_succes_tag{} + [&]() __SCOPE_GUARD_ACTION_NOEXCEPT -> void +#define SCOPE_SUCCESS ATTR_MAYBE_UNUSED const MAKE_SCOPE_SUCCESS(__SCOPE_GUARD_STR_CONCAT(__scope_guard_succes__object_, __SCOPE_GUARD_COUNTER)) +#define WITH_SCOPE_SUCCESS(guard) __SCOPE_GUARD_WITH(SCOPE_SUCCESS{guard}) + +// DEFER executing action on scope exit. +#define MAKE_DEFER(name) MAKE_SCOPE_EXIT(name) +#define DEFER SCOPE_EXIT +#define WITH_DEFER(guard) WITH_SCOPE_EXIT(guard) + +#endif // NEARGYE_SCOPE_GUARD_HPP \ No newline at end of file diff --git a/ML_LES/ML_LES/ML_LES_Model/tf_utils.C b/ML_LES/ML_LES/ML_LES_Model/tf_utils.C new file mode 100755 index 0000000..0507349 --- /dev/null +++ b/ML_LES/ML_LES/ML_LES_Model/tf_utils.C @@ -0,0 +1,340 @@ +#include "tf_utils.H" + +#include +#include +#include +#include +#include + +namespace tf_utils { + +// Tensorflow requirements +const char* TFDataTypeToString(TF_DataType data_type) { + switch (data_type) { + case TF_FLOAT: + return "TF_FLOAT"; + case TF_DOUBLE: + return "TF_DOUBLE"; + case TF_INT32: + return "TF_INT32"; + case TF_UINT8: + return "TF_UINT8"; + case TF_INT16: + return "TF_INT16"; + case TF_INT8: + return "TF_INT8"; + case TF_STRING: + return "TF_STRING"; + case TF_COMPLEX64: + return "TF_COMPLEX64"; + case TF_INT64: + return "TF_INT64"; + case TF_BOOL: + return "TF_BOOL"; + case TF_QINT8: + return "TF_QINT8"; + case TF_QUINT8: + return "TF_QUINT8"; + case TF_QINT32: + return "TF_QINT32"; + case TF_BFLOAT16: + return "TF_BFLOAT16"; + case TF_QINT16: + return "TF_QINT16"; + case TF_QUINT16: + return "TF_QUINT16"; + case TF_UINT16: + return "TF_UINT16"; + case TF_COMPLEX128: + return "TF_COMPLEX128"; + case TF_HALF: + return "TF_HALF"; + case TF_RESOURCE: + return "TF_RESOURCE"; + case TF_VARIANT: + return "TF_VARIANT"; + case TF_UINT32: + return "TF_UINT32"; + case TF_UINT64: + return "TF_UINT64"; + default: + return "Unknown"; + } +} + +static void DeallocateBuffer(void* data, size_t) { + std::free(data); +} + +static TF_Buffer* ReadBufferFromFile(const char* file) { + std::ifstream f(file, std::ios::binary); + if (f.fail() || !f.is_open()) { + return nullptr; + } + + f.seekg(0, std::ios::end); + const auto fsize = f.tellg(); + f.seekg(0, std::ios::beg); + + if (fsize < 1) { + f.close(); + return nullptr; + } + + char* data = static_cast(std::malloc(fsize)); + f.read(data, fsize); + f.close(); + + TF_Buffer* buf = TF_NewBuffer(); + buf->data = data; + buf->length = fsize; + buf->data_deallocator = DeallocateBuffer; + + return buf; +} + +TF_Graph* LoadGraph(const char* graphPath) { + if (graphPath == nullptr) { + return nullptr; + } + + TF_Buffer* buffer = ReadBufferFromFile(graphPath); + if (buffer == nullptr) { + return nullptr; + } + + TF_Graph* graph = TF_NewGraph(); + TF_Status* status = TF_NewStatus(); + TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions(); + + TF_GraphImportGraphDef(graph, buffer, opts, status); + TF_DeleteImportGraphDefOptions(opts); + TF_DeleteBuffer(buffer); + + if (TF_GetCode(status) != TF_OK) { + TF_DeleteGraph(graph); + graph = nullptr; + } + + TF_DeleteStatus(status); + + return graph; +} + +void DeleteGraph(TF_Graph* graph) { + TF_DeleteGraph(graph); +} + +void DeleteSession(TF_Session* session) { + TF_Status* status = TF_NewStatus(); + TF_CloseSession(session, status); + if (TF_GetCode(status) != TF_OK) { + TF_CloseSession(session, status); + } + TF_DeleteSession(session, status); + if (TF_GetCode(status) != TF_OK) { + TF_DeleteSession(session, status); + } + TF_DeleteStatus(status); +} + +TF_Session* CreateSession(TF_Graph* graph) { + TF_Status* status = TF_NewStatus(); + TF_SessionOptions* options = TF_NewSessionOptions(); + TF_Session* session = TF_NewSession(graph, options, status); + TF_DeleteSessionOptions(options); + + if (TF_GetCode(status) != TF_OK) { + DeleteSession(session); + TF_DeleteStatus(status); + return nullptr; + } + TF_DeleteStatus(status); + + return session; +} + +TF_Code RunSession(TF_Session* session, + const TF_Output* inputs, TF_Tensor* const* input_tensors, std::size_t ninputs, + const TF_Output* outputs, TF_Tensor** output_tensors, std::size_t noutputs) { + if (session == nullptr || + inputs == nullptr || input_tensors == nullptr || + outputs == nullptr || output_tensors == nullptr) { + return TF_INVALID_ARGUMENT; + } + + TF_Status* status = TF_NewStatus(); + TF_SessionRun(session, + nullptr, // Run options. + inputs, input_tensors, static_cast(ninputs), // Input tensors, input tensor values, number of inputs. + outputs, output_tensors, static_cast(noutputs), // Output tensors, output tensor values, number of outputs. + nullptr, 0, // Target operations, number of targets. + nullptr, // Run metadata. + status // Output status. + ); + + TF_Code code = TF_GetCode(status); + TF_DeleteStatus(status); + return code; +} + +TF_Code RunSession(TF_Session* session, + const std::vector& inputs, const std::vector& input_tensors, + const std::vector& outputs, std::vector& output_tensors) { + return RunSession(session, + inputs.data(), input_tensors.data(), input_tensors.size(), + outputs.data(), output_tensors.data(), output_tensors.size()); +} + +TF_Tensor* CreateTensor(TF_DataType data_type, + const std::int64_t* dims, std::size_t num_dims, + const void* data, std::size_t len) { + if (dims == nullptr) { + return nullptr; + } + + TF_Tensor* tensor = TF_AllocateTensor(data_type, dims, static_cast(num_dims), len); + if (tensor == nullptr) { + return nullptr; + } + + void* tensor_data = TF_TensorData(tensor); + if (tensor_data == nullptr) { + TF_DeleteTensor(tensor); + return nullptr; + } + + if (data != nullptr) { + std::memcpy(tensor_data, data, std::min(len, TF_TensorByteSize(tensor))); + } + + // delete static_cast(tensor_data); + + return tensor; +} + +TF_Tensor* CreateEmptyTensor(TF_DataType data_type, const std::int64_t* dims, std::size_t num_dims){ + return CreateTensor(data_type, dims, num_dims, nullptr, 0); +} + +TF_Tensor* CreateEmptyTensor(TF_DataType data_type, const std::vector& dims) { + return CreateEmptyTensor(data_type, dims.data(), dims.size()); +} + +void DeleteTensor(TF_Tensor* tensor) { + if (tensor != nullptr) { + TF_DeleteTensor(tensor); + } +} + +void DeleteTensors(const std::vector& tensors) { + for (auto t : tensors) { + TF_DeleteTensor(t); + } +} + +void SetTensorsData(TF_Tensor* tensor, const void* data, std::size_t len) { + void* tensor_data = TF_TensorData(tensor); + if (tensor_data != nullptr) { + std::memcpy(tensor_data, data, std::min(len, TF_TensorByteSize(tensor))); + } +} + +// Some operations using tf_utils +void PrintInputs(TF_Graph*, TF_Operation* op) { + const int num_inputs = TF_OperationNumInputs(op); + + for (int i = 0; i < num_inputs; ++i) { + const TF_Input input = {op, i}; + const TF_DataType type = TF_OperationInputType(input); + std::cout << "Input: " << i << " type: " << TFDataTypeToString(type) << std::endl; + } +} + +void PrintOutputs(TF_Graph* graph, TF_Operation* op) { + const int num_outputs = TF_OperationNumOutputs(op); + TF_Status* status = TF_NewStatus(); + + for (int i = 0; i < num_outputs; ++i) { + const TF_Output output = {op, i}; + const TF_DataType type = TF_OperationOutputType(output); + const int num_dims = TF_GraphGetTensorNumDims(graph, output, status); + + if (TF_GetCode(status) != TF_OK) { + std::cout << "Can't get tensor dimensionality" << std::endl; + continue; + } + + std::cout << " dims: " << num_dims; + + if (num_dims <= 0) { + std::cout << " []" << std::endl;; + continue; + } + + std::vector dims(num_dims); + + std::cout << "Output: " << i << " type: " << TFDataTypeToString(type); + TF_GraphGetTensorShape(graph, output, dims.data(), num_dims, status); + + if (TF_GetCode(status) != TF_OK) { + std::cout << "Can't get get tensor shape" << std::endl; + continue; + } + + std::cout << " ["; + for (int d = 0; d < num_dims; ++d) { + std::cout << dims[d]; + if (d < num_dims - 1) { + std::cout << ", "; + } + } + std::cout << "]" << std::endl; + } + + TF_DeleteStatus(status); +} + +void PrintTensorInfo(TF_Graph* graph, const char* layer_name) { + std::cout << "Tensor: " << layer_name; + TF_Operation* op = TF_GraphOperationByName(graph, layer_name); + + if (op == nullptr) { + std::cout << "Could not get " << layer_name << std::endl; + return; + } + + const int num_inputs = TF_OperationNumInputs(op); + const int num_outputs = TF_OperationNumOutputs(op); + std::cout << " inputs: " << num_inputs << " outputs: " << num_outputs << std::endl; + + PrintInputs(graph, op); + + PrintOutputs(graph, op); +} + +void PrintOp(TF_Graph* graph) { + TF_Operation* op; + std::size_t pos = 0; + + while ((op = TF_GraphNextOperation(graph, &pos)) != nullptr) { + const char* name = TF_OperationName(op); + const char* type = TF_OperationOpType(op); + const char* device = TF_OperationDevice(op); + + const int num_outputs = TF_OperationNumOutputs(op); + const int num_inputs = TF_OperationNumInputs(op); + + std::cout << pos << ": " << name << " type: " << type << " device: " << device << " number inputs: " << num_inputs << " number outputs: " << num_outputs << std::endl; + + PrintInputs(graph, op); + PrintOutputs(graph, op); + std::cout << std::endl; + } +} + +} // namespace tf_utils + +#if defined(_MSC_VER) +# pragma warning(pop) +#endif \ No newline at end of file diff --git a/OF_Model/tf_utils.H b/ML_LES/ML_LES/ML_LES_Model/tf_utils.H old mode 100644 new mode 100755 similarity index 100% rename from OF_Model/tf_utils.H rename to ML_LES/ML_LES/ML_LES_Model/tf_utils.H diff --git a/ML_LES/README.md b/ML_LES/README.md new file mode 100644 index 0000000..616bed0 --- /dev/null +++ b/ML_LES/README.md @@ -0,0 +1,7 @@ +# Work in progress +A neural network surrogate to the Dynamic Smagorinsky model +ML_LES - Machine learned surrogate +D_SMA - Localized dynamic Smagorinsky +UNS - No model + +Runs with `pimpleFoam` \ No newline at end of file diff --git a/ML_LES/UNS_Validation/0/U b/ML_LES/UNS_Validation/0/U new file mode 100755 index 0000000..f9cc26b --- /dev/null +++ b/ML_LES/UNS_Validation/0/U @@ -0,0 +1,60073 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volVectorField; + location "1000"; + object U; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 1 -1 0 0 0 0]; + +internalField nonuniform List +60000 +( +(0.0107864 0.000249703 -0.00476747) +(0.00902904 0.000117458 -0.00405083) +(0.00756391 6.51604e-05 -0.00211429) +(0.00625766 1.18193e-05 -0.000141315) +(0.00582921 -0.000119649 0.000559694) +(0.006293 -0.000195876 0.000428585) +(0.00646146 -0.000161025 0.000344736) +(0.00588379 -8.08387e-05 0.000997234) +(0.00521528 -2.58731e-05 0.0018189) +(0.00506792 1.25882e-05 0.00205846) +(0.00528329 -2.26371e-05 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type cyclic; + } + inout2_half1 + { + type cyclic; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/0/k b/ML_LES/UNS_Validation/0/k new file mode 100755 index 0000000..40ca04f --- /dev/null +++ b/ML_LES/UNS_Validation/0/k @@ -0,0 +1,60073 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object k; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -2 0 0 0 0]; + +internalField nonuniform List +60000 +( +3.94306e-05 +3.00853e-05 +2.21131e-05 +1.54717e-05 +1.07369e-05 +8.81747e-06 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+5.269e-05 +7.68206e-05 +0.000106097 +) +; + +boundaryField +{ + bottomWall + { + type fixedValue; + value uniform 0; + } + topWall + { + type fixedValue; + value uniform 0; + } + sides1_half0 + { + type cyclic; + } + sides1_half1 + { + type cyclic; + } + sides2_half0 + { + type cyclic; + } + sides2_half1 + { + type cyclic; + } + inout1_half0 + { + type cyclic; + } + inout1_half1 + { + type cyclic; + } + inout2_half0 + { + type cyclic; + } + inout2_half1 + { + type cyclic; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/0/nuTilda b/ML_LES/UNS_Validation/0/nuTilda new file mode 100755 index 0000000..a8ddf50 --- /dev/null +++ b/ML_LES/UNS_Validation/0/nuTilda @@ -0,0 +1,69 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5 | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1"; + object nuTilda; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -1 0 0 0 0]; + +internalField uniform 0; + +boundaryField +{ + bottomWall + { + type fixedValue; + value uniform 0; + } + topWall + { + type fixedValue; + value uniform 0; + } + sides1_half0 + { + type cyclic; + } + sides2_half0 + { + type cyclic; + } + inout1_half0 + { + type cyclic; + } + inout2_half0 + { + type cyclic; + } + sides2_half1 + { + type cyclic; + } + sides1_half1 + { + type cyclic; + } + inout1_half1 + { + type cyclic; + } + inout2_half1 + { + type cyclic; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/0/nut b/ML_LES/UNS_Validation/0/nut new file mode 100755 index 0000000..58fc5b3 --- /dev/null +++ b/ML_LES/UNS_Validation/0/nut @@ -0,0 +1,60071 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object nut; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -1 0 0 0 0]; + +internalField nonuniform List +60000 +( +3.21326e-08 +2.2299e-07 +3.32864e-07 +5.10662e-07 +6.43821e-07 +4.78013e-07 +1.73643e-07 +-1.16484e-07 +-2.77889e-07 +-2.47343e-07 +-5.67778e-08 +1.06284e-07 +1.31505e-07 +1.3946e-07 +1.18022e-07 +7.5891e-09 +-1.13711e-07 +-1.55148e-07 +-6.14348e-08 +5.1394e-08 +6.17946e-08 +6.10477e-08 +9.31066e-08 +4.88387e-08 +-7.25943e-08 +-1.23497e-07 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type cyclic; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/0/p b/ML_LES/UNS_Validation/0/p new file mode 100755 index 0000000..7b02619 --- /dev/null +++ b/ML_LES/UNS_Validation/0/p @@ -0,0 +1,60071 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class volScalarField; + location "1000"; + object p; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +dimensions [0 2 -2 0 0 0 0]; + +internalField nonuniform List +60000 +( +-0.000168142 +-0.000188879 +-0.000160074 +-0.000167958 +-9.48166e-05 +3.42481e-06 +1.94088e-05 +-5.01153e-05 +-0.000108898 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+| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object fvOptions; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +momentumSource +{ + type meanVelocityForce; + active yes; + + meanVelocityForceCoeffs + { + selectionMode all; + + fields (U); + Ubar (0.1335 0 0); + } +} + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/constant/polyMesh/boundary b/ML_LES/UNS_Validation/constant/polyMesh/boundary new file mode 100755 index 0000000..cb75ec9 --- /dev/null +++ b/ML_LES/UNS_Validation/constant/polyMesh/boundary @@ -0,0 +1,116 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class polyBoundaryMesh; + location "constant/polyMesh"; + object boundary; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +10 +( + bottomWall + { + type wall; + inGroups 1(wall); + nFaces 1200; + startFace 175300; + } + topWall + { + type wall; + inGroups 1(wall); + nFaces 1200; + startFace 176500; + } + sides1_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 177700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides1_half1; + } + sides1_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 178700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides1_half0; + } + sides2_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 179700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides2_half1; + } + sides2_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 1000; + startFace 180700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch sides2_half0; + } + inout1_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 181700; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout1_half1; + } + inout1_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 182450; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout1_half0; + } + inout2_half0 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 183200; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout2_half1; + } + inout2_half1 + { + type cyclic; + inGroups 1(cyclic); + nFaces 750; + startFace 183950; + matchTolerance 0.0001; + transform unknown; + neighbourPatch inout2_half0; + } +) + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/constant/polyMesh/faces b/ML_LES/UNS_Validation/constant/polyMesh/faces new file mode 100755 index 0000000..bfb140c --- /dev/null +++ b/ML_LES/UNS_Validation/constant/polyMesh/faces @@ -0,0 +1,184724 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class faceList; + location "constant/polyMesh"; + object faces; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +184700 +( +4(1 42 1108 1067) +4(41 1107 1108 42) +4(1066 1067 1108 1107) +4(2 43 1109 1068) +4(42 1108 1109 43) +4(1067 1068 1109 1108) +4(3 44 1110 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************************************************************************* // diff --git a/ML_LES/UNS_Validation/constant/polyMesh/neighbour b/ML_LES/UNS_Validation/constant/polyMesh/neighbour new file mode 100755 index 0000000..f807274 --- /dev/null +++ b/ML_LES/UNS_Validation/constant/polyMesh/neighbour @@ -0,0 +1,175325 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 5.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class labelList; + note "nPoints: 64821 nCells: 60000 nFaces: 184700 nInternalFaces: 175300"; + location "constant/polyMesh"; + object neighbour; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +175300 +( +1 +40 +1000 +2 +41 +1001 +3 +42 +1002 +4 +43 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object owner; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +184700 +( +0 +0 +0 +1 +1 +1 +2 +2 +2 +3 +3 +3 +4 +4 +4 +5 +5 +5 +6 +6 +6 +7 +7 +7 +8 +8 +8 +9 +9 +9 +10 +10 +10 +11 +11 +11 +12 +12 +12 +13 +13 +13 +14 +14 +14 +15 +15 +15 +16 +16 +16 +17 +17 +17 +18 +18 +18 +19 +19 +19 +20 +20 +20 +21 +21 +21 +22 +22 +22 +23 +23 +23 +24 +24 +24 +25 +25 +25 +26 +26 +26 +27 +27 +27 +28 +28 +28 +29 +29 +29 +30 +30 +30 +31 +31 +31 +32 +32 +32 +33 +33 +33 +34 +34 +34 +35 +35 +35 +36 +36 +36 +37 +37 +37 +38 +38 +38 +39 +39 +40 +40 +40 +41 +41 +41 +42 +42 +42 +43 +43 +43 +44 +44 +44 +45 +45 +45 +46 +46 +46 +47 +47 +47 +48 +48 +48 +49 +49 +49 +50 +50 +50 +51 +51 +51 +52 +52 +52 +53 +53 +53 +54 +54 +54 +55 +55 +55 +56 +56 +56 +57 +57 +57 +58 +58 +58 +59 +59 +59 +60 +60 +60 +61 +61 +61 +62 +62 +62 +63 +63 +63 +64 +64 +64 +65 +65 +65 +66 +66 +66 +67 +67 +67 +68 +68 +68 +69 +69 +69 +70 +70 +70 +71 +71 +71 +72 +72 +72 +73 +73 +73 +74 +74 +74 +75 +75 +75 +76 +76 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anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class vectorField; + location "constant/polyMesh"; + object points; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + +64821 +( +(0 0 0) +(0.1 0 0) +(0.2 0 0) +(0.3 0 0) +(0.4 0 0) +(0.5 0 0) +(0.6 0 0) +(0.7 0 0) +(0.8 0 0) +(0.9 0 0) +(1 0 0) +(1.1 0 0) +(1.2 0 0) +(1.3 0 0) +(1.4 0 0) +(1.5 0 0) +(1.6 0 0) +(1.7 0 0) +(1.8 0 0) +(1.9 0 0) +(2 0 0) +(2.1 0 0) +(2.2 0 0) +(2.3 0 0) +(2.4 0 0) +(2.5 0 0) +(2.6 0 0) +(2.7 0 0) +(2.8 0 0) +(2.9 0 0) +(3 0 0) +(3.1 0 0) +(3.2 0 0) +(3.3 0 0) +(3.4 0 0) +(3.5 0 0) +(3.6 0 0) +(3.7 0 0) +(3.8 0 0) +(3.9 0 0) +(4 0 0) +(0 0.009600014954 0) +(0.1 0.009600014954 0) +(0.2 0.009600014954 0) +(0.3 0.009600014954 0) +(0.4 0.009600014954 0) +(0.5 0.009600014954 0) +(0.6 0.009600014954 0) +(0.7 0.009600014954 0) +(0.8 0.009600014954 0) +(0.9 0.009600014954 0) +(1 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1.968114097 2) +(3.4 1.968114097 2) +(3.5 1.968114097 2) +(3.6 1.968114097 2) +(3.7 1.968114097 2) +(3.8 1.968114097 2) +(3.9 1.968114097 2) +(4 1.968114097 2) +(0 1.979808232 2) +(0.1 1.979808232 2) +(0.2 1.979808232 2) +(0.3 1.979808232 2) +(0.4 1.979808232 2) +(0.5 1.979808232 2) +(0.6 1.979808232 2) +(0.7 1.979808232 2) +(0.8 1.979808232 2) +(0.9 1.979808232 2) +(1 1.979808232 2) +(1.1 1.979808232 2) +(1.2 1.979808232 2) +(1.3 1.979808232 2) +(1.4 1.979808232 2) +(1.5 1.979808232 2) +(1.6 1.979808232 2) +(1.7 1.979808232 2) +(1.8 1.979808232 2) +(1.9 1.979808232 2) +(2 1.979808232 2) +(2.1 1.979808232 2) +(2.2 1.979808232 2) +(2.3 1.979808232 2) +(2.4 1.979808232 2) +(2.5 1.979808232 2) +(2.6 1.979808232 2) +(2.7 1.979808232 2) +(2.8 1.979808232 2) +(2.9 1.979808232 2) +(3 1.979808232 2) +(3.1 1.979808232 2) +(3.2 1.979808232 2) +(3.3 1.979808232 2) +(3.4 1.979808232 2) +(3.5 1.979808232 2) +(3.6 1.979808232 2) +(3.7 1.979808232 2) +(3.8 1.979808232 2) +(3.9 1.979808232 2) +(4 1.979808232 2) +(0 1.990402377 2) +(0.1 1.990402377 2) +(0.2 1.990402377 2) +(0.3 1.990402377 2) +(0.4 1.990402377 2) +(0.5 1.990402377 2) +(0.6 1.990402377 2) +(0.7 1.990402377 2) +(0.8 1.990402377 2) +(0.9 1.990402377 2) +(1 1.990402377 2) +(1.1 1.990402377 2) +(1.2 1.990402377 2) +(1.3 1.990402377 2) +(1.4 1.990402377 2) +(1.5 1.990402377 2) +(1.6 1.990402377 2) +(1.7 1.990402377 2) +(1.8 1.990402377 2) +(1.9 1.990402377 2) +(2 1.990402377 2) +(2.1 1.990402377 2) +(2.2 1.990402377 2) +(2.3 1.990402377 2) +(2.4 1.990402377 2) +(2.5 1.990402377 2) +(2.6 1.990402377 2) +(2.7 1.990402377 2) +(2.8 1.990402377 2) +(2.9 1.990402377 2) +(3 1.990402377 2) +(3.1 1.990402377 2) +(3.2 1.990402377 2) +(3.3 1.990402377 2) +(3.4 1.990402377 2) +(3.5 1.990402377 2) +(3.6 1.990402377 2) +(3.7 1.990402377 2) +(3.8 1.990402377 2) +(3.9 1.990402377 2) +(4 1.990402377 2) +(0 2 2) +(0.1 2 2) +(0.2 2 2) +(0.3 2 2) +(0.4 2 2) +(0.5 2 2) +(0.6 2 2) +(0.7 2 2) +(0.8 2 2) +(0.9 2 2) +(1 2 2) +(1.1 2 2) +(1.2 2 2) +(1.3 2 2) +(1.4 2 2) +(1.5 2 2) +(1.6 2 2) +(1.7 2 2) +(1.8 2 2) +(1.9 2 2) +(2 2 2) +(2.1 2 2) +(2.2 2 2) +(2.3 2 2) +(2.4 2 2) +(2.5 2 2) +(2.6 2 2) +(2.7 2 2) +(2.8 2 2) +(2.9 2 2) +(3 2 2) +(3.1 2 2) +(3.2 2 2) +(3.3 2 2) +(3.4 2 2) +(3.5 2 2) +(3.6 2 2) +(3.7 2 2) +(3.8 2 2) +(3.9 2 2) +(4 2 2) +) + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/constant/postChannelDict b/ML_LES/UNS_Validation/constant/postChannelDict new file mode 100755 index 0000000..e1a5d5c --- /dev/null +++ b/ML_LES/UNS_Validation/constant/postChannelDict @@ -0,0 +1,29 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object postChannelDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +// Seed patches to start layering from +patches ( bottomWall ); + +// Direction in which the layers are +component y; + +// Is the mesh symmetric? If so average(symmetric fields) or +// subtract(asymmetric) contributions from both halves +symmetric true; + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/constant/transportProperties b/ML_LES/UNS_Validation/constant/transportProperties new file mode 100755 index 0000000..2f0f1ec --- /dev/null +++ b/ML_LES/UNS_Validation/constant/transportProperties @@ -0,0 +1,24 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object transportProperties; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +Ubar [0 1 -1 0 0 0 0] (0.1335 0 0); + +transportModel Newtonian; + +nu [0 2 -1 0 0 0 0] 2e-05; + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/constant/turbulenceProperties b/ML_LES/UNS_Validation/constant/turbulenceProperties new file mode 100755 index 0000000..195c406 --- /dev/null +++ b/ML_LES/UNS_Validation/constant/turbulenceProperties @@ -0,0 +1,100 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "constant"; + object turbulenceProperties; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +simulationType LES; + +LES +{ + LESModel ML_LES; + + turbulence on; + + printCoeffs on; + + delta cubeRootVol; + + filter simple; + + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + dynamicSmagorinskyCoeffs + { + filter simple; + } + + PrandtlCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } + + Cdelta 0.158; + } + + vanDriestCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } + + Aplus 26; + Cdelta 0.158; + } + + smoothCoeffs + { + delta cubeRootVol; + cubeRootVolCoeffs + { + deltaCoeff 1; + } + + maxDeltaRatio 1.1; + } +} + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/system/blockMeshDict b/ML_LES/UNS_Validation/system/blockMeshDict new file mode 100755 index 0000000..26ae6ee --- /dev/null +++ b/ML_LES/UNS_Validation/system/blockMeshDict @@ -0,0 +1,115 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + object blockMeshDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +convertToMeters 1; + +vertices +( + (0 0 0) + (4 0 0) + (0 1 0) + (4 1 0) + (0 2 0) + (4 2 0) + (0 0 2) + (4 0 2) + (0 1 2) + (4 1 2) + (0 2 2) + (4 2 2) +); + +blocks +( + hex (0 1 3 2 6 7 9 8) (40 25 30) simpleGrading (1 10.7028 1) + hex (2 3 5 4 8 9 11 10) (40 25 30) simpleGrading (1 0.0934 1) +); + +edges +( +); + +boundary +( + bottomWall + { + type wall; + faces ((0 1 7 6)); + } + topWall + { + type wall; + faces ((4 10 11 5)); + } + + sides1_half0 + { + type cyclic; + neighbourPatch sides1_half1; + faces ((0 2 3 1)); + } + sides1_half1 + { + type cyclic; + neighbourPatch sides1_half0; + faces ((6 7 9 8)); + } + + sides2_half0 + { + type cyclic; + neighbourPatch sides2_half1; + faces ((2 4 5 3)); + } + sides2_half1 + { + type cyclic; + neighbourPatch sides2_half0; + faces ((8 9 11 10)); + } + + inout1_half0 + { + type cyclic; + neighbourPatch inout1_half1; + faces ((1 3 9 7)); + } + inout1_half1 + { + type cyclic; + neighbourPatch inout1_half0; + faces ((0 6 8 2)); + } + + inout2_half0 + { + type cyclic; + neighbourPatch inout2_half1; + faces ((3 5 11 9)); + } + inout2_half1 + { + type cyclic; + neighbourPatch inout2_half0; + faces ((2 8 10 4)); + } +); + +mergePatchPairs +( +); + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/system/controlDict b/ML_LES/UNS_Validation/system/controlDict new file mode 100755 index 0000000..2daaa1c --- /dev/null +++ b/ML_LES/UNS_Validation/system/controlDict @@ -0,0 +1,80 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object controlDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +libs +( +"ML_LES.so" +); + +application pimpleFoam; + +startFrom startTime; + +startTime 0; + +stopAt endTime; + +endTime 1000; + +deltaT 0.2; + +writeControl timeStep; + +writeInterval 200; + +purgeWrite 0; + +writeFormat ascii; + +writePrecision 6; + +writeCompression off; + +timeFormat general; + +timePrecision 6; + +runTimeModifiable true; + +functions +{ + fieldAverage1 + { + type fieldAverage; + libs ("libfieldFunctionObjects.so"); + writeControl writeTime; + + fields + ( + U + { + mean on; + prime2Mean on; + base time; + } + + p + { + mean on; + prime2Mean on; + base time; + } + ); + } +} + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/system/decomposeParDict b/ML_LES/UNS_Validation/system/decomposeParDict new file mode 100755 index 0000000..b469469 --- /dev/null +++ b/ML_LES/UNS_Validation/system/decomposeParDict @@ -0,0 +1,135 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + note "mesh decomposition control dictionary"; + object decomposeParDict; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +numberOfSubdomains 4; + +//- Keep owner and neighbour on same processor for faces in zones: +// preserveFaceZones (heater solid1 solid3); + +//- Keep owner and neighbour on same processor for faces in patches: +// (makes sense only for cyclic patches) +//preservePatches (cyclic_half0 cyclic_half1); + +//- Keep all of faceSet on a single processor. This puts all cells +// connected with a point, edge or face on the same processor. +// (just having face connected cells might not guarantee a balanced +// decomposition) +// The processor can be -1 (the decompositionMethod chooses the processor +// for a good load balance) or explicitly provided (upsets balance). +//singleProcessorFaceSets ((f0 -1)); + + +//- Use the volScalarField named here as a weight for each cell in the +// decomposition. For example, use a particle population field to decompose +// for a balanced number of particles in a lagrangian simulation. +// weightField dsmcRhoNMean; + +// method scotch; +// method hierarchical; +method simple; +// method metis; +// method manual; +// method multiLevel; +// method structured; // does 2D decomposition of structured mesh + +multiLevelCoeffs +{ + // Decomposition methods to apply in turn. This is like hierarchical but + // fully general - every method can be used at every level. + + level0 + { + numberOfSubdomains 64; + //method simple; + //simpleCoeffs + //{ + // n (2 1 1); + // delta 0.001; + //} + method scotch; + } + level1 + { + numberOfSubdomains 4; + method scotch; + } +} + +// Desired output + +simpleCoeffs +{ + n (1 2 2); + delta 0.001; +} + +hierarchicalCoeffs +{ + n (1 2 1); + delta 0.001; + order xyz; +} + +metisCoeffs +{ + /* + processorWeights + ( + 1 + 1 + 1 + 1 + ); + */ +} + +scotchCoeffs +{ + //processorWeights + //( + // 1 + // 1 + // 1 + // 1 + //); + //writeGraph true; + //strategy "b"; +} + +manualCoeffs +{ + dataFile "decompositionData"; +} + +structuredCoeffs +{ + // Patches to do 2D decomposition on. Structured mesh only; cells have + // to be in 'columns' on top of patches. + patches (bottomPatch); +} + +//// Is the case distributed? Note: command-line argument -roots takes +//// precedence +//distributed yes; +//// Per slave (so nProcs-1 entries) the directory above the case. +//roots +//( +// "/tmp" +// "/tmp" +//); + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/system/fvSchemes b/ML_LES/UNS_Validation/system/fvSchemes new file mode 100755 index 0000000..78fc3ba --- /dev/null +++ b/ML_LES/UNS_Validation/system/fvSchemes @@ -0,0 +1,60 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object fvSchemes; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +ddtSchemes +{ + default backward; +} + +gradSchemes +{ + default Gauss linear; +} + +divSchemes +{ + default none; + div(phi,U) Gauss linear; + div(phi,k) Gauss limitedLinear 1; + div(phi,B) Gauss limitedLinear 1; + div(B) Gauss linear; + div(phi,nuTilda) Gauss limitedLinear 1; + div((nuEff*dev2(T(grad(U))))) Gauss linear; +} + +laplacianSchemes +{ + default Gauss linear corrected; +} + +interpolationSchemes +{ + default linear; +} + +snGradSchemes +{ + default corrected; +} + +wallDist +{ + method meshWave; +} + + +// ************************************************************************* // diff --git a/ML_LES/UNS_Validation/system/fvSolution b/ML_LES/UNS_Validation/system/fvSolution new file mode 100755 index 0000000..348f3e5 --- /dev/null +++ b/ML_LES/UNS_Validation/system/fvSolution @@ -0,0 +1,62 @@ +/*--------------------------------*- C++ -*----------------------------------*\ +| ========= | | +| \\ / F ield | OpenFOAM: The Open Source CFD Toolbox | +| \\ / O peration | Version: 4.x | +| \\ / A nd | Web: www.OpenFOAM.org | +| \\/ M anipulation | | +\*---------------------------------------------------------------------------*/ +FoamFile +{ + version 2.0; + format ascii; + class dictionary; + location "system"; + object fvSolution; +} +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +solvers +{ + p + { + solver GAMG; + tolerance 0; + relTol 0.1; + smoother GaussSeidel; + } + + pFinal + { + $p; + smoother DICGaussSeidel; + tolerance 1e-06; + relTol 0; + } + + "(U|k|nuTilda)" + { + solver smoothSolver; + smoother symGaussSeidel; + tolerance 1e-05; + relTol 0.1; + } + + "(U|k|nuTilda)Final" + { + $U; + tolerance 1e-05; + relTol 0; + } +} + +PIMPLE +{ + nOuterCorrectors 1; + nCorrectors 2; + nNonOrthogonalCorrectors 0; + pRefCell 1001; + pRefValue 0; +} + + +// ************************************************************************* // diff --git a/OF_Model/ML_SA_CG.C b/ML_RANS/OF_Model/ML_SA_CG.C similarity index 100% rename from OF_Model/ML_SA_CG.C rename to ML_RANS/OF_Model/ML_SA_CG.C diff --git a/OF_Model/ML_SA_CG.H b/ML_RANS/OF_Model/ML_SA_CG.H similarity index 100% rename from OF_Model/ML_SA_CG.H rename to ML_RANS/OF_Model/ML_SA_CG.H diff --git a/OF_Model/Make/files b/ML_RANS/OF_Model/Make/files similarity index 100% rename from OF_Model/Make/files rename to ML_RANS/OF_Model/Make/files diff --git a/OF_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.C.dep b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.C.dep similarity index 100% rename from OF_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.C.dep rename to ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.C.dep diff --git a/OF_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o similarity index 100% rename from OF_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o rename to ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/maketurbulentTransportModels.o diff --git a/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/options b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/options new file mode 100644 index 0000000..82b88bd --- /dev/null +++ b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/options @@ -0,0 +1,63 @@ +# 1 "Make/options" +# 1 "" +# 1 "" +# 8 "" +# 1 "/usr/include/stdc-predef.h" 1 3 4 + +# 17 "/usr/include/stdc-predef.h" 3 4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +# 8 "" 2 +# 1 "Make/options" +EXE_INC = -I$(LIB_SRC)/TurbulenceModels/turbulenceModels/lnInclude -I$(LIB_SRC)/TurbulenceModels/incompressible/lnInclude -I$(LIB_SRC)/transportModels -I$(LIB_SRC)/finiteVolume/lnInclude -I$(LIB_SRC)/meshTools/lnInclude + + + + + + +LIB_LIBS = -lincompressibleTransportModels -lturbulenceModels -lfiniteVolume -lmeshTools -ltensorflow -lstdc++ diff --git a/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/sourceFiles b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/sourceFiles new file mode 100644 index 0000000..1da348b --- /dev/null +++ b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/sourceFiles @@ -0,0 +1,3 @@ +SOURCE = \ +maketurbulentTransportModels.C \ +tf_utils.C diff --git a/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep new file mode 100644 index 0000000..15f8289 --- /dev/null +++ b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/tf_utils.C.dep @@ -0,0 +1,5 @@ +$(OBJECTS_DIR)/tf_utils.C.dep: \ +tf_utils.C \ +tf_utils.H \ + + diff --git a/OF_Model/Make/linux64GccDPInt64Opt/tf_utils.o b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/tf_utils.o similarity index 100% rename from OF_Model/Make/linux64GccDPInt64Opt/tf_utils.o rename to ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/tf_utils.o diff --git a/OF_Model/Make/linux64GccDPInt64Opt/variables b/ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/variables similarity index 100% rename from OF_Model/Make/linux64GccDPInt64Opt/variables rename to ML_RANS/OF_Model/Make/linux64GccDPInt64Opt/variables diff --git a/ML_RANS/OF_Model/Make/options b/ML_RANS/OF_Model/Make/options new file mode 100644 index 0000000..d9e21fc --- /dev/null +++ b/ML_RANS/OF_Model/Make/options @@ -0,0 +1,14 @@ +EXE_INC = \ + -I$(LIB_SRC)/TurbulenceModels/turbulenceModels/lnInclude \ + -I$(LIB_SRC)/TurbulenceModels/incompressible/lnInclude \ + -I$(LIB_SRC)/transportModels \ + -I$(LIB_SRC)/finiteVolume/lnInclude \ + -I$(LIB_SRC)/meshTools/lnInclude \ + +LIB_LIBS = \ + -lincompressibleTransportModels \ + -lturbulenceModels \ + -lfiniteVolume \ + -lmeshTools \ + -ltensorflow \ + -lstdc++ diff --git a/ML_RANS/OF_Model/lnInclude/ML_SA_CG.C b/ML_RANS/OF_Model/lnInclude/ML_SA_CG.C new file mode 120000 index 0000000..c2cd873 --- /dev/null +++ b/ML_RANS/OF_Model/lnInclude/ML_SA_CG.C @@ -0,0 +1 @@ +../ML_SA_CG.C \ No newline at end of file diff --git a/ML_RANS/OF_Model/lnInclude/ML_SA_CG.H b/ML_RANS/OF_Model/lnInclude/ML_SA_CG.H new file mode 120000 index 0000000..3978277 --- /dev/null +++ b/ML_RANS/OF_Model/lnInclude/ML_SA_CG.H @@ -0,0 +1 @@ +../ML_SA_CG.H \ No newline at end of file diff --git a/ML_RANS/OF_Model/lnInclude/maketurbulentTransportModels.C b/ML_RANS/OF_Model/lnInclude/maketurbulentTransportModels.C new file mode 120000 index 0000000..0b8de36 --- /dev/null +++ b/ML_RANS/OF_Model/lnInclude/maketurbulentTransportModels.C @@ -0,0 +1 @@ +../maketurbulentTransportModels.C \ No newline at end of file diff --git a/ML_RANS/OF_Model/lnInclude/tf_utils.C b/ML_RANS/OF_Model/lnInclude/tf_utils.C new file mode 120000 index 0000000..28a9278 --- /dev/null +++ b/ML_RANS/OF_Model/lnInclude/tf_utils.C @@ -0,0 +1 @@ +../tf_utils.C \ No newline at end of file diff --git a/ML_RANS/OF_Model/lnInclude/tf_utils.H b/ML_RANS/OF_Model/lnInclude/tf_utils.H new file mode 120000 index 0000000..b478e26 --- /dev/null +++ b/ML_RANS/OF_Model/lnInclude/tf_utils.H @@ -0,0 +1 @@ +../tf_utils.H \ No newline at end of file diff --git a/OF_Model/lnInclude/maketurbulentTransportModels.C b/ML_RANS/OF_Model/maketurbulentTransportModels.C similarity index 100% rename from OF_Model/lnInclude/maketurbulentTransportModels.C rename to ML_RANS/OF_Model/maketurbulentTransportModels.C diff --git a/OF_Model/tf_utils.C b/ML_RANS/OF_Model/tf_utils.C similarity index 100% rename from OF_Model/tf_utils.C rename to ML_RANS/OF_Model/tf_utils.C diff --git a/ML_RANS/OF_Model/tf_utils.H b/ML_RANS/OF_Model/tf_utils.H new file mode 100644 index 0000000..1d3979a --- /dev/null +++ b/ML_RANS/OF_Model/tf_utils.H @@ -0,0 +1,107 @@ +// Licensed under the MIT License . +// SPDX-License-Identifier: MIT +// Copyright (c) 2018 - 2019 Daniil Goncharov . +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in all +// copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +// SOFTWARE. + +#pragma once + +#if defined(_MSC_VER) +# if !defined(COMPILER_MSVC) +# define COMPILER_MSVC // Set MSVC visibility of exported symbols in the shared library. +# endif +# pragma warning(push) +# pragma warning(disable : 4190) +#endif + +#include // TensorFlow C API header +#include +#include +#include + +namespace tf_utils { + +TF_Graph* LoadGraph(const char* graphPath); + +void DeleteGraph(TF_Graph* graph); + +TF_Session* CreateSession(TF_Graph* graph); + +void DeleteSession(TF_Session* session); + +TF_Code RunSession(TF_Session* session, + const TF_Output* inputs, TF_Tensor* const* input_tensors, std::size_t ninputs, + const TF_Output* outputs, TF_Tensor** output_tensors, std::size_t noutputs); + +TF_Code RunSession(TF_Session* session, + const std::vector& inputs, const std::vector& input_tensors, + const std::vector& outputs, std::vector& output_tensors); + +TF_Tensor* CreateTensor(TF_DataType data_type, + const std::int64_t* dims, std::size_t num_dims, + const void* data, std::size_t len); + +template +TF_Tensor* CreateTensor(TF_DataType data_type, const std::vector& dims, const std::vector& data) { + return CreateTensor(data_type, + dims.data(), dims.size(), + data.data(), data.size() * sizeof(T)); +} + +TF_Tensor* CreateEmptyTensor(TF_DataType data_type, const std::int64_t* dims, std::size_t num_dims); + +TF_Tensor* CreateEmptyTensor(TF_DataType data_type, const std::vector& dims); + +void DeleteTensor(TF_Tensor* tensor); + +void DeleteTensors(const std::vector& tensors); + +void SetTensorsData(TF_Tensor* tensor, const void* data, std::size_t len); + +template +void SetTensorsData(TF_Tensor* tensor, const std::vector& data) { + SetTensorsData(tensor, data.data(), data.size() * sizeof(T)); +} + +template +std::vector GetTensorsData(const TF_Tensor* tensor) { + auto data = static_cast(TF_TensorData(tensor)); + if (data == nullptr) { + return {}; + } + + return {data, data + (TF_TensorByteSize(tensor) / TF_DataTypeSize(TF_TensorType(tensor)))}; +} + +template +std::vector> GetTensorsData(const std::vector& tensors) { + std::vector> data; + data.reserve(tensors.size()); + for (const auto t : tensors) { + data.push_back(GetTensorsData(t)); + } + + return data; +} + +} // namespace tf_utils + +#if defined(_MSC_VER) +# pragma warning(pop) +#endif \ No newline at end of file diff --git a/ML_RANS/README.md b/ML_RANS/README.md new file mode 100644 index 0000000..b784049 --- /dev/null +++ b/ML_RANS/README.md @@ -0,0 +1,65 @@ +# Data-driven RANS using Tensorflow + +Please ensure that the TensorFlow C API and OpenFOAM 5 have been installed properly before following the steps in this tutorial. This may be found in the root `README.md`. In this document we will rely on the approach laid out in [arXiv:1910.10878](https://arxiv.org/pdf/1910.10878) where a deep neural network is utilized to predict steady-state turbulent viscosities of the Spalart-Allmaras (SA) model. The goal of this article is to avoid solving the one-extra equation that SA requires but replace it with a point deep-learning framework for acceleration. The training data for this experiment is available [here](https://drive.google.com/file/d/102IZP01xfitnW_wu6_UUfBpA3bCK_qkT/view?usp=sharing) and correspond to inputs of initial conditions (given by potentialFoam), mesh coordinates and step height and outputs of the steady-state turbulent viscosity. We will try to train a neural network to learn $M_2$ in Equation 4 of the article. + +## Step 1: Train and export a model in Python + +While the C API of TensorFlow can be used to train models, it is far easier to use the Python API for training and porting a model to OpenFOAM using an immutable set of trained model parameters. From the aforementioned link, grab the training data `Total_dataset.npy` and place it in the `Training/` directory. Our model is defined in the `get_model` function of `ML_Model.py`. An inspection shows us that this is a simple fully-connected architecture defined using `tensorflow.keras`: +``` +# Define model architecture here +ph_input = keras.Input(shape=(num_inputs,),name='input_placeholder') +hidden_layer = keras.layers.Dense(num_neurons,activation='relu')(ph_input) + +for layer in range(num_layers): + hidden_layer = keras.layers.Dense(num_neurons,activation='relu')(hidden_layer) + +output = keras.layers.Dense(num_outputs,activation='linear',name='output_value')(hidden_layer) + +model = keras.Model(inputs=[ph_input],outputs=[output]) +keras.utils.plot_model(model, 'ml_model.png', show_shapes=True) +my_adam = keras.optimizers.Adam(lr=0.001, decay=0.0) + +model.compile(optimizer=my_adam,loss={'output_value': 'mean_squared_error'},metrics=[r2_2]) +``` +Feel free to tweak this architecture and assess its effects on the quality of the learning. The model can be trained by invoking `python ML_Model.py` from a terminal (with the right python environment active) following which a `model.h5` is generated with the best trainable parameters. Some _a prior_ assessment of the learning can be performed by running `python Training_Assessment.py` which generates several plots showing the scatter accuracy, convergence of training and probability density functions of true versus predicted turbulent eddy viscosities (our targets). + +An important function in `ML_Model.py` is the `freeze_session()` function which is instrumental for saving the trained model to disk as a `*.pb` file. This may then be read in by the TensorFlow C API in OpenFOAM. Our trained model (both in `.h5` and `.pb` format along with plots are available in the original `Original/` subdirectory.) + + +## Step 2: Make modifications to Spalart-Allmaras turbulence model + +Once our machine learned turbulence model is trained and exported to disk as a `.pb` file, we need to read it into OpenFOAM to compute steady-state turbulent eddy viscosities with it. The following material is based on tutorials for compiling new turbulence models in OpenFOAM and some preliminary reading is encouraged from the following links if you are not super familiar with this: +1. [How to implement your own turbulence model - by H. Nilsson](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=6&cad=rja&uact=8&ved=2ahUKEwjw-8WEuKXpAhVHHs0KHYEfCBcQFjAFegQIBRAB&url=http%3A%2F%2Fwww.tfd.chalmers.se%2F~hani%2Fkurser%2FOS_CFD_2010%2FimplementTurbulenceModel.pdf&usg=AOvVaw2MvuKXW200b75D7tNAmhZg) +2. [How to add a turbulence model in OpenFOAM-3.0.0 - by H. Kassem](http://hassankassem.me/posts/newturbulencemodel/) +3. [How to add a turbulence model in OpenFOAM-5.0.0 - by H. Kassem](http://hassankassem.me/posts/newturbulencemodel6/) + +Now that you know how to go about with the addition of a new turbulence model to OpenFOAM - we can go ahead and make additions to it to interact with TensorFlow 1.15. Within `OF_Model/` you can find our novel turbulence model set up and ready to go. We are going to call this model `ML_SA_CG` ("Machine-learned Spalart-Allmaras for Cross-Geometry applications"). You will notice the following three lines (291-293) in the constructor of the `ML_SA_CG.C` file +``` +graph_ = tf_utils::LoadGraph("./ML_SA_CG.pb"); +input_ph_ = {TF_GraphOperationByName(graph_, "input_placeholder"), 0}; +output_ = {TF_GraphOperationByName(graph_, "output_value/BiasAdd"), 0}; +``` +The first `graph_` tells OpenFOAM to load in the neural network model as graph, the second line defines an operation `input_ph_` for funneling data into the network to make predictions and `output_` tells TensorFlow about the operation it should run to retrieve the predictions of the framework. These variables are defined in lines 87-89 of `ML_SA_CG.H` following OpenFOAM convention. Note that TensorFlow functionality is embedded into this new turbulence model in line 43 of the header file by stating `#include "tf_utils.H"`. Both `tf_utils.C` and `tf_utils.H` are based on this fantastic repository provided for constructing neural networks using the TF C API by [Daniil Goncharov](https://github.com/Neargye). Also note the contents of `Make/files` where we have also included `tf_utils.C` to ensure TensorFlow functionality is properly recognized and line 13 of `Make/options` where `libtensorflow.so` is provided for linking during compile time. Note that the absolute path to `libtensorflow.so` must be provided if you did not install TensorFlow in the default location. + +## Step 3: Compile ML turbulence model + +Now all that is left to do is to run `wclean && wmake libso .` from the `OF_Model/` directory. This will create a `ML_SA_CG.so` file in `$FOAM_USER_LIBBIN`. + +## Step 4: Make changes to simpleFoam + +The original framework in this project required a minor modification to `simpleFoam` to switch turbulence model deployment at each iteration towards steady-state. This is because the machine learning framework has been trained to _predict_ the steady-state turbulent eddy viscosity. To that end, we modify `simpleFoam` to `simpleFoam_ML` to make an eddy-viscosity prediction at `t=0` and then solve only pressure and velocity equations after. Once again, recommended reading for understanding this may be found in [OpenFOAM programming tutorial by Tommaso Lucchini](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=14&cad=rja&uact=8&ved=2ahUKEwjKy6-XvqXpAhVIQ80KHYisDikQFjANegQICRAB&url=http%3A%2F%2Fwww.tfd.chalmers.se%2F~hani%2Fkurser%2FOS_CFD_2009%2FprogrammingTutorial.pdf&usg=AOvVaw2H5hYleW_cI6CN0inVnBWQ). + +To create a new version of `simpleFoam` that predicts the eddy viscosity only once and then solves solely pressure and velocity, we have followed the steps in the previous link and have created a new solver saved in `simpleFOAM_ML/`. You may incorporate this model into OpenFOAM as follows: +1. From `ML_RANS/` copy `simpleFOAM_ML` to `$WM_PROJECT_DIR` by running `cp -r simpleFoam_ML/ $WM_PROJECT_DIR/applications/solvers/simpleFoam_ML`. +2. Compile this new solver using `cd $WM_PROJECT_DIR/applications/solvers/simpleFoam_ML && wclean && wmake` +3. Test that the new solver is compiled and ready to use with `simpleFoam_ML -help` + +## Step 5: Deploy for test case + +Finally, now that a new solver and a new turbulence model are ready to deploy. We can run a case to test them. This case is provided in `Testing/`. To run the case successfully, we have made a few changes to incorporate the new solver and the neural network model. These are +1. Line 18 in `system/controlDict` where the application is now `simpleFoam_ML` +2. Line 58 in `system/controlDict` where we link to `ML_SA_CG.so`, our new turbulence model, during run time. +3. Line 22 in `constant/turbulenceProperties` where our `RASModel` is now `ML_SA_CG`. +4. Finally, we have a text file `means` in the `Testing/` which corresponds to the ML scaling factors for preprocessing our physical inputs. This is read by line 388 of `ML_SA_CG.C`. The values within this file have to manually be set by the means and standard deviations of the training inputs and training outputs. These numbers are produced in `Training/` when the ML model is trained in Python in a similarly named `means` file. Note that these numbers may be copied into the OpenFOAM read-friendly format located within the case directory. +5. The case directory should also have `ML_SA_CG.pb` created within `Training/` (i.e., our neural network model saved to disk) +6. If the previous steps in this tutorial have been followed accurately, you may now run `simpleFoam_ML` from the case directory and reproduce the results shown in the second half of the paper. \ No newline at end of file diff --git a/Testing/0/U b/ML_RANS/Testing/0/U similarity index 100% rename from Testing/0/U rename to ML_RANS/Testing/0/U diff --git a/Testing/0/U_orig b/ML_RANS/Testing/0/U_orig similarity index 100% rename from Testing/0/U_orig rename to ML_RANS/Testing/0/U_orig diff --git a/Testing/0/epsilon b/ML_RANS/Testing/0/epsilon similarity index 100% rename from Testing/0/epsilon rename to ML_RANS/Testing/0/epsilon diff --git a/Testing/0/gradu b/ML_RANS/Testing/0/gradu similarity index 100% rename from Testing/0/gradu rename to ML_RANS/Testing/0/gradu diff --git a/Testing/0/gradv b/ML_RANS/Testing/0/gradv similarity index 100% rename from Testing/0/gradv rename to ML_RANS/Testing/0/gradv diff --git a/Testing/0/k b/ML_RANS/Testing/0/k similarity index 100% rename from Testing/0/k rename to ML_RANS/Testing/0/k diff --git a/Testing/0/nuTilda b/ML_RANS/Testing/0/nuTilda similarity index 100% rename from Testing/0/nuTilda rename to ML_RANS/Testing/0/nuTilda diff --git a/Testing/0/nut b/ML_RANS/Testing/0/nut similarity index 100% rename from Testing/0/nut rename to ML_RANS/Testing/0/nut diff --git a/Testing/0/nut_ml_ b/ML_RANS/Testing/0/nut_ml_ similarity index 100% rename from Testing/0/nut_ml_ rename to ML_RANS/Testing/0/nut_ml_ diff --git a/Testing/0/omega b/ML_RANS/Testing/0/omega similarity index 100% rename from Testing/0/omega rename to ML_RANS/Testing/0/omega diff --git a/Testing/0/p b/ML_RANS/Testing/0/p similarity index 100% rename from Testing/0/p rename to ML_RANS/Testing/0/p diff --git a/Testing/0/p_orig b/ML_RANS/Testing/0/p_orig similarity index 100% rename from Testing/0/p_orig rename to ML_RANS/Testing/0/p_orig diff --git a/Testing/0/rot b/ML_RANS/Testing/0/rot similarity index 100% rename from Testing/0/rot rename to ML_RANS/Testing/0/rot diff --git a/Testing/0/umag b/ML_RANS/Testing/0/umag similarity index 100% rename from Testing/0/umag rename to ML_RANS/Testing/0/umag diff --git a/Testing/0/vortz b/ML_RANS/Testing/0/vortz similarity index 100% rename from Testing/0/vortz rename to ML_RANS/Testing/0/vortz diff --git a/Testing/ML_SA_CG.pb b/ML_RANS/Testing/ML_SA_CG.pb similarity index 100% rename from Testing/ML_SA_CG.pb rename to ML_RANS/Testing/ML_SA_CG.pb diff --git a/Testing/constant/polyMesh/boundary b/ML_RANS/Testing/constant/polyMesh/boundary similarity index 100% rename from Testing/constant/polyMesh/boundary rename to ML_RANS/Testing/constant/polyMesh/boundary diff --git a/Testing/constant/polyMesh/faces b/ML_RANS/Testing/constant/polyMesh/faces similarity index 100% rename from Testing/constant/polyMesh/faces rename to ML_RANS/Testing/constant/polyMesh/faces diff --git a/Testing/constant/polyMesh/neighbour b/ML_RANS/Testing/constant/polyMesh/neighbour similarity index 100% rename from Testing/constant/polyMesh/neighbour rename to ML_RANS/Testing/constant/polyMesh/neighbour diff --git a/Testing/constant/polyMesh/owner b/ML_RANS/Testing/constant/polyMesh/owner similarity index 100% rename from Testing/constant/polyMesh/owner rename to ML_RANS/Testing/constant/polyMesh/owner diff --git a/Testing/constant/polyMesh/points b/ML_RANS/Testing/constant/polyMesh/points similarity index 100% rename from Testing/constant/polyMesh/points rename to ML_RANS/Testing/constant/polyMesh/points diff --git a/Testing/constant/transportProperties b/ML_RANS/Testing/constant/transportProperties similarity index 100% rename from Testing/constant/transportProperties rename to ML_RANS/Testing/constant/transportProperties diff --git a/Testing/constant/turbulenceProperties b/ML_RANS/Testing/constant/turbulenceProperties similarity index 100% rename from Testing/constant/turbulenceProperties rename to ML_RANS/Testing/constant/turbulenceProperties diff --git a/Testing/means b/ML_RANS/Testing/means similarity index 100% rename from Testing/means rename to ML_RANS/Testing/means diff --git a/Testing/system/blockMeshDict b/ML_RANS/Testing/system/blockMeshDict similarity index 100% rename from Testing/system/blockMeshDict rename to ML_RANS/Testing/system/blockMeshDict diff --git a/Testing/system/controlDict b/ML_RANS/Testing/system/controlDict similarity index 100% rename from Testing/system/controlDict rename to ML_RANS/Testing/system/controlDict diff --git a/Testing/system/decomposeParDict b/ML_RANS/Testing/system/decomposeParDict similarity index 100% rename from Testing/system/decomposeParDict rename to ML_RANS/Testing/system/decomposeParDict diff --git a/Testing/system/fvSchemes b/ML_RANS/Testing/system/fvSchemes similarity index 100% rename from Testing/system/fvSchemes rename to ML_RANS/Testing/system/fvSchemes diff --git a/Testing/system/fvSolution b/ML_RANS/Testing/system/fvSolution similarity index 100% rename from Testing/system/fvSolution rename to ML_RANS/Testing/system/fvSolution diff --git a/Testing/system/pressureCoefficient b/ML_RANS/Testing/system/pressureCoefficient similarity index 100% rename from Testing/system/pressureCoefficient rename to ML_RANS/Testing/system/pressureCoefficient diff --git a/Testing/system/sampleCp b/ML_RANS/Testing/system/sampleCp similarity index 100% rename from Testing/system/sampleCp rename to ML_RANS/Testing/system/sampleCp diff --git a/Testing/system/sampleDict b/ML_RANS/Testing/system/sampleDict similarity index 100% rename from Testing/system/sampleDict rename to ML_RANS/Testing/system/sampleDict diff --git a/Testing/system/stressComponents b/ML_RANS/Testing/system/stressComponents similarity index 100% rename from Testing/system/stressComponents rename to ML_RANS/Testing/system/stressComponents diff --git a/Training/ML_Model.py b/ML_RANS/Training/ML_Model.py similarity index 91% rename from Training/ML_Model.py rename to ML_RANS/Training/ML_Model.py index 4563f32..1d2bd33 100644 --- a/Training/ML_Model.py +++ b/ML_RANS/Training/ML_Model.py @@ -158,3 +158,20 @@ def load_graph(frozen_graph_filename): num_inputs, training_inputs, num_outputs, training_outputs = load_data() model = get_model(num_inputs,num_outputs,6,40) fit_model(training_inputs,training_outputs,model,500) + + # Load data for scaling factors + total_dataset = np.load('Total_dataset.npy') + + # Save the statistics of the data + means = np.mean(total_dataset,axis=0).reshape(1,np.shape(total_dataset)[1]) + stds = np.std(total_dataset,axis=0).reshape(1,np.shape(total_dataset)[1]) + + # Concatenate + op_data = np.concatenate((means,stds),axis=0) + np.savetxt('means.txt',op_data, delimiter=' ') + + # Make sure you copy this to the means within OpenFOAM case directory in the proper format + print('Means:') + print(means) + print('Stds:') + print(stds) diff --git a/ML_RANS/Training/Original/ML_SA_CG.pb b/ML_RANS/Training/Original/ML_SA_CG.pb new file mode 100644 index 0000000..ac31d19 Binary files /dev/null and b/ML_RANS/Training/Original/ML_SA_CG.pb differ diff --git a/ML_RANS/Training/Original/M_2_Convergence.png b/ML_RANS/Training/Original/M_2_Convergence.png new file mode 100644 index 0000000..b4b0b6e Binary files /dev/null and b/ML_RANS/Training/Original/M_2_Convergence.png differ diff --git a/ML_RANS/Training/Original/M_2_Density.png b/ML_RANS/Training/Original/M_2_Density.png new file mode 100644 index 0000000..f55eb9f Binary files /dev/null and b/ML_RANS/Training/Original/M_2_Density.png differ diff --git a/ML_RANS/Training/Original/M_2_Scatter.png b/ML_RANS/Training/Original/M_2_Scatter.png new file mode 100644 index 0000000..36bf67a Binary files /dev/null and b/ML_RANS/Training/Original/M_2_Scatter.png differ diff --git a/ML_RANS/Training/Original/means.txt b/ML_RANS/Training/Original/means.txt new file mode 100644 index 0000000..1e840c3 --- /dev/null +++ b/ML_RANS/Training/Original/means.txt @@ -0,0 +1,2 @@ +4.079686917592934492e+01 -1.208243514188240608e+00 -3.261139731191171065e-01 5.640163886112639635e-02 1.382706624699276299e+00 1.613694987496413135e-03 +6.916749517394584323e+00 5.413467829647387930e+00 5.565129236052048789e-01 4.110704482615899769e-02 5.507583080990012814e-01 2.269512471019279141e-03 diff --git a/ML_RANS/Training/Original/model.h5 b/ML_RANS/Training/Original/model.h5 new file mode 100644 index 0000000..f804827 Binary files /dev/null and b/ML_RANS/Training/Original/model.h5 differ diff --git a/ML_RANS/Training/Original/training.log b/ML_RANS/Training/Original/training.log new file mode 100644 index 0000000..2616b3c --- /dev/null +++ b/ML_RANS/Training/Original/training.log @@ -0,0 +1,42 @@ +epoch,loss,r2_2,val_loss,val_r2_2 +0,0.12758192817415068,0.87113905,0.013923393619900418,0.9860371 +1,0.011881681556120023,0.98800194,0.009610728817042053,0.99036986 +2,0.008287696255834283,0.9916331,0.00778591070722046,0.9922021 +3,0.006981150706296668,0.9929521,0.0065527671798191415,0.9933967 +4,0.006335587141919279,0.993604,0.005794333755153181,0.9941972 +5,0.005893773835247409,0.99404347,0.004999670890260661,0.9949769 +6,0.004921922328292818,0.995026,0.0056372242606031854,0.994348 +7,0.004745794394283797,0.9952043,0.0043123846724027705,0.99566865 +8,0.004378393012427611,0.9955755,0.003799723524649753,0.99620134 +9,0.004349931141924469,0.9955975,0.004412034709101604,0.9955754 +10,0.004069855138569299,0.9958913,0.0043935100225416185,0.99558634 +11,0.004029168706203947,0.99593425,0.004473734490410512,0.9955074 +12,0.0038336234767774808,0.99613076,0.0034814140561075417,0.996509 +13,0.003963172976403568,0.9959921,0.003768762429524539,0.9962236 +14,0.0038407297816150676,0.99612224,0.0030983435822365644,0.9968861 +15,0.00352567238586283,0.9964358,0.002947798109100532,0.99704 +16,0.0037010499024635914,0.9962524,0.003991138457353525,0.9959957 +17,0.003651569597706122,0.99630314,0.0036285088084444736,0.99634784 +18,0.0036572713863461436,0.9962972,0.0030567682853338872,0.9969386 +19,0.0035837038623716317,0.99637264,0.003290982176069098,0.99669814 +20,0.0033383741201851043,0.9966275,0.003919546898084795,0.9960723 +21,0.003424746763269911,0.99654084,0.003672146569727529,0.9963039 +22,0.0033745072948255456,0.99658364,0.0028368760996298373,0.9971559 +23,0.003329299248159701,0.9966323,0.003106716557101735,0.9968765 +24,0.0032286442187804427,0.99673235,0.0034727711093429903,0.9965148 +25,0.0033407745936955076,0.99662536,0.0030960166474626304,0.99688673 +26,0.0032407804961330712,0.99672574,0.0029266573163974456,0.99706936 +27,0.0031461587252630823,0.9968169,0.0031345292209712544,0.9968621 +28,0.003089449151141327,0.99687254,0.003926477636115621,0.9960421 +29,0.0032684620220498934,0.99668837,0.0026659458079733344,0.99732554 +30,0.003023321331201913,0.9969424,0.0026134022238646793,0.99738157 +31,0.0030903154286302335,0.9968755,0.003354884600546134,0.9966412 +32,0.0029963659919870003,0.99696714,0.0033945947137953344,0.99658495 +33,0.0031481163235729135,0.99681824,0.0033994254183912003,0.9965923 +34,0.002929991722785721,0.9970374,0.0026552394505727074,0.9973428 +35,0.003002475777071299,0.9969615,0.003867184627556355,0.9961135 +36,0.0029405761732805135,0.9970225,0.002984011742990682,0.9969944 +37,0.003055741922683415,0.99691063,0.0026824496733767918,0.99730885 +38,0.0029782864413631124,0.9969884,0.0029273184986274565,0.9970585 +39,0.0029417548850435116,0.99702686,0.0026185037657369898,0.99737763 +40,0.00302506566617999,0.9969426,0.004667791211161863,0.99531573 diff --git a/Training/Training_Assessment.py b/ML_RANS/Training/Training_Assessment.py similarity index 82% rename from Training/Training_Assessment.py rename to ML_RANS/Training/Training_Assessment.py index ced6246..a9abbec 100644 --- a/Training/Training_Assessment.py +++ b/ML_RANS/Training/Training_Assessment.py @@ -82,3 +82,21 @@ def load_graph(frozen_graph_filename): plt.hist(true_op[:,0].flatten(),range=(r_min,r_max),bins=30,label='True',alpha=0.5) plt.legend() plt.show() + + # Plot the convergence of learning + loss_log = np.loadtxt('training.log',skiprows=1,delimiter=',') + + fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(7,6)) + ax[0].plot(loss_log[:,0],loss_log[:,1],label='Training loss') + ax[0].plot(loss_log[:,0],loss_log[:,3],label='Validation loss') + ax[0].set_xlabel('Epoch') + ax[0].set_ylabel('Mean-squared error') + + ax[1].plot(loss_log[:,0],loss_log[:,2],label=r'Training $R^2$') + ax[1].plot(loss_log[:,0],loss_log[:,4],label=r'Validation $R^2$') + ax[1].set_xlabel('Epoch') + ax[1].set_ylabel(r'$R^2$') + + plt.tight_layout() + plt.show() + diff --git a/ML_RANS/simpleFoam_ML/Make/files b/ML_RANS/simpleFoam_ML/Make/files new file mode 100755 index 0000000..fd4ac75 --- /dev/null +++ b/ML_RANS/simpleFoam_ML/Make/files @@ -0,0 +1,3 @@ +simpleFoam_ML.C + +EXE = $(FOAM_APPBIN)/simpleFoam_ML diff --git a/ML_RANS/simpleFoam_ML/Make/options b/ML_RANS/simpleFoam_ML/Make/options new file mode 100755 index 0000000..1d9ded8 --- /dev/null +++ b/ML_RANS/simpleFoam_ML/Make/options @@ -0,0 +1,18 @@ +EXE_INC = \ + -I$(LIB_SRC)/TurbulenceModels/turbulenceModels/lnInclude \ + -I$(LIB_SRC)/TurbulenceModels/incompressible/lnInclude \ + -I$(LIB_SRC)/transportModels \ + -I$(LIB_SRC)/transportModels/incompressible/singlePhaseTransportModel \ + -I$(LIB_SRC)/finiteVolume/lnInclude \ + -I$(LIB_SRC)/meshTools/lnInclude \ + -I$(LIB_SRC)/sampling/lnInclude + + +EXE_LIBS = \ + -lturbulenceModels \ + -lincompressibleTurbulenceModels \ + -lincompressibleTransportModels \ + -lfiniteVolume \ + -lmeshTools \ + -lfvOptions \ + -lsampling diff --git a/ML_RANS/simpleFoam_ML/UEqn.H b/ML_RANS/simpleFoam_ML/UEqn.H new file mode 100755 index 0000000..81e83b5 --- /dev/null +++ b/ML_RANS/simpleFoam_ML/UEqn.H @@ -0,0 +1,24 @@ + // Momentum predictor + + MRF.correctBoundaryVelocity(U); + + tmp tUEqn + ( + fvm::div(phi, U) + + MRF.DDt(U) + + turbulence->divDevReff(U) + == + fvOptions(U) + ); + fvVectorMatrix& UEqn = tUEqn.ref(); + + UEqn.relax(); + + fvOptions.constrain(UEqn); + + if (simple.momentumPredictor()) + { + solve(UEqn == -fvc::grad(p)); + + fvOptions.correct(U); + } diff --git a/ML_RANS/simpleFoam_ML/createFields.H b/ML_RANS/simpleFoam_ML/createFields.H new file mode 100755 index 0000000..86789ed --- /dev/null +++ b/ML_RANS/simpleFoam_ML/createFields.H @@ -0,0 +1,45 @@ +Info<< "Reading field p\n" << endl; +volScalarField p +( + IOobject + ( + "p", + runTime.timeName(), + mesh, + IOobject::MUST_READ, + IOobject::AUTO_WRITE + ), + mesh +); + +Info<< "Reading field U\n" << endl; +volVectorField U +( + IOobject + ( + "U", + runTime.timeName(), + mesh, + IOobject::MUST_READ, + IOobject::AUTO_WRITE + ), + mesh +); + +#include "createPhi.H" + + +label pRefCell = 0; +scalar pRefValue = 0.0; +setRefCell(p, simple.dict(), pRefCell, pRefValue); +mesh.setFluxRequired(p.name()); + + +singlePhaseTransportModel laminarTransport(U, phi); + +autoPtr turbulence +( + incompressible::turbulenceModel::New(U, phi, laminarTransport) +); + +#include "createMRF.H" diff --git a/ML_RANS/simpleFoam_ML/pEqn.H b/ML_RANS/simpleFoam_ML/pEqn.H new file mode 100755 index 0000000..7e394e9 --- /dev/null +++ b/ML_RANS/simpleFoam_ML/pEqn.H @@ -0,0 +1,50 @@ +{ + volScalarField rAU(1.0/UEqn.A()); + volVectorField HbyA(constrainHbyA(rAU*UEqn.H(), U, p)); + surfaceScalarField phiHbyA("phiHbyA", fvc::flux(HbyA)); + MRF.makeRelative(phiHbyA); + adjustPhi(phiHbyA, U, p); + + tmp rAtU(rAU); + + if (simple.consistent()) + { + rAtU = 1.0/(1.0/rAU - UEqn.H1()); + phiHbyA += + fvc::interpolate(rAtU() - rAU)*fvc::snGrad(p)*mesh.magSf(); + HbyA -= (rAU - rAtU())*fvc::grad(p); + } + + tUEqn.clear(); + + // Update the pressure BCs to ensure flux consistency + constrainPressure(p, U, phiHbyA, rAtU(), MRF); + + // Non-orthogonal pressure corrector loop + while (simple.correctNonOrthogonal()) + { + fvScalarMatrix pEqn + ( + fvm::laplacian(rAtU(), p) == fvc::div(phiHbyA) + ); + + pEqn.setReference(pRefCell, pRefValue); + + pEqn.solve(); + + if (simple.finalNonOrthogonalIter()) + { + phi = phiHbyA - pEqn.flux(); + } + } + + #include "continuityErrs.H" + + // Explicitly relax pressure for momentum corrector + p.relax(); + + // Momentum corrector + U = HbyA - rAtU()*fvc::grad(p); + U.correctBoundaryConditions(); + fvOptions.correct(U); +} diff --git a/ML_RANS/simpleFoam_ML/simpleFoam_ML.C b/ML_RANS/simpleFoam_ML/simpleFoam_ML.C new file mode 100755 index 0000000..515105a --- /dev/null +++ b/ML_RANS/simpleFoam_ML/simpleFoam_ML.C @@ -0,0 +1,86 @@ +/*---------------------------------------------------------------------------*\ + ========= | + \\ / F ield | OpenFOAM: The Open Source CFD Toolbox + \\ / O peration | + \\ / A nd | Copyright (C) 2011-2016 OpenFOAM Foundation + \\/ M anipulation | +------------------------------------------------------------------------------- +License + This file is part of OpenFOAM. + + OpenFOAM is free software: you can redistribute it and/or modify it + under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + OpenFOAM is distributed in the hope that it will be useful, but WITHOUT + ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or + FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + for more details. + + You should have received a copy of the GNU General Public License + along with OpenFOAM. If not, see . + +Application + simpleFoam + +Description + Steady-state solver for incompressible, turbulent flow, using the SIMPLE + algorithm. + +\*---------------------------------------------------------------------------*/ + +#include "fvCFD.H" +#include "singlePhaseTransportModel.H" +#include "turbulentTransportModel.H" +#include "simpleControl.H" +#include "fvOptions.H" + +// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + +int main(int argc, char *argv[]) +{ + #include "postProcess.H" + + #include "setRootCase.H" + #include "createTime.H" + #include "createMesh.H" + #include "createControl.H" + #include "createFields.H" + #include "createFvOptions.H" + #include "initContinuityErrs.H" + + turbulence->validate(); + turbulence->correct(); + + // * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // + + Info<< "\nStarting time loop\n" << endl; + + while (simple.loop()) + { + Info<< "Time = " << runTime.timeName() << nl << endl; + + // --- Pressure-velocity SIMPLE corrector + { + #include "UEqn.H" + #include "pEqn.H" + } + + laminarTransport.correct(); +// turbulence->correct(); + + runTime.write(); + + Info<< "ExecutionTime = " << runTime.elapsedCpuTime() << " s" + << " ClockTime = " << runTime.elapsedClockTime() << " s" + << nl << endl; + } + + Info<< "End\n" << endl; + + return 0; +} + + +// ************************************************************************* // diff --git a/OF_Model/lnInclude/ML_SA_CG.C b/OF_Model/lnInclude/ML_SA_CG.C deleted file mode 100644 index 8a3c1f7..0000000 --- a/OF_Model/lnInclude/ML_SA_CG.C +++ /dev/null @@ -1,508 +0,0 @@ -/*---------------------------------------------------------------------------*\ - ========= | - \\ / F ield | OpenFOAM: The Open Source CFD Toolbox - \\ / O peration | - \\ / A nd | Copyright (C) 2011-2016 OpenFOAM Foundation - \\/ M anipulation | -------------------------------------------------------------------------------- -License - This file is part of OpenFOAM. - - OpenFOAM is free software: you can redistribute it and/or modify it - under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - OpenFOAM is distributed in the hope that it will be useful, but WITHOUT - ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or - FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License - for more details. - - You should have received a copy of the GNU General Public License - along with OpenFOAM. If not, see . - -\*---------------------------------------------------------------------------*/ -#include -#include "ML_SA_CG.H" -#include "fvOptions.H" -#include "bound.H" -#include "wallDist.H" -#include -#include - -#include -#include -#include -#include -#include - -// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // -namespace Foam -{ -namespace RASModels -{ - -// * * * * * * * * * * * * Protected Member Functions * * * * * * * * * * * // - -template -tmp ML_SA_CG::chi() const -{ - return nuTilda_/this->nu(); -} - - -template -tmp ML_SA_CG::fv1 -( - const volScalarField& chi -) const -{ - const volScalarField chi3(pow3(chi)); - return chi3/(chi3 + pow3(Cv1_)); -} - - -template -tmp ML_SA_CG::fv2 -( - const volScalarField& chi, - const volScalarField& fv1 -) const -{ - return 1.0 - chi/(1.0 + chi*fv1); -} - - -template -tmp ML_SA_CG::Stilda -( - const volScalarField& chi, - const volScalarField& fv1 -) const -{ - volScalarField Omega(::sqrt(2.0)*mag(skew(fvc::grad(this->U_)))); - - return - ( - max - ( - Omega - + fv2(chi, fv1)*nuTilda_/sqr(kappa_*y_), - Cs_*Omega - ) - ); -} - - -template -tmp ML_SA_CG::fw -( - const volScalarField& Stilda -) const -{ - volScalarField r - ( - min - ( - nuTilda_ - /( - max - ( - Stilda, - dimensionedScalar("SMALL", Stilda.dimensions(), SMALL) - ) - *sqr(kappa_*y_) - ), - scalar(10.0) - ) - ); - r.boundaryFieldRef() == 0.0; - - const volScalarField g(r + Cw2_*(pow6(r) - r)); - - return g*pow((1.0 + pow6(Cw3_))/(pow6(g) + pow6(Cw3_)), 1.0/6.0); -} - - -template -void ML_SA_CG::correctNut -( - const volScalarField& fv1 -) -{ - this->nut_ = nuTilda_*fv1; - this->nut_.correctBoundaryConditions(); - fv::options::New(this->mesh_).correct(this->nut_); - - BasicTurbulenceModel::correctNut(); -} - - -template -void ML_SA_CG::correctNut() -{ - correctNut(fv1(this->chi())); -} - -// * * * * * * * * * * * * * * * * Constructors * * * * * * * * * * * * * * // - -template -ML_SA_CG::ML_SA_CG -( - const alphaField& alpha, - const rhoField& rho, - const volVectorField& U, - const surfaceScalarField& alphaRhoPhi, - const surfaceScalarField& phi, - const transportModel& transport, - const word& propertiesName, - const word& type, - int omp_num_threads_ -) -: - eddyViscosity> - ( - type, - alpha, - rho, - U, - alphaRhoPhi, - phi, - transport, - propertiesName - ), - - sigmaNut_ - ( - dimensioned::lookupOrAddToDict - ( - "sigmaNut", - this->coeffDict_, - 0.66666 - ) - ), - kappa_ - ( - dimensioned::lookupOrAddToDict - ( - "kappa", - this->coeffDict_, - 0.41 - ) - ), - - Cb1_ - ( - dimensioned::lookupOrAddToDict - ( - "Cb1", - this->coeffDict_, - 0.1355 - ) - ), - Cb2_ - ( - dimensioned::lookupOrAddToDict - ( - "Cb2", - this->coeffDict_, - 0.622 - ) - ), - Cw1_(Cb1_/sqr(kappa_) + (1.0 + Cb2_)/sigmaNut_), - Cw2_ - ( - dimensioned::lookupOrAddToDict - ( - "Cw2", - this->coeffDict_, - 0.3 - ) - ), - Cw3_ - ( - dimensioned::lookupOrAddToDict - ( - "Cw3", - this->coeffDict_, - 2.0 - ) - ), - Cv1_ - ( - dimensioned::lookupOrAddToDict - ( - "Cv1", - this->coeffDict_, - 7.1 - ) - ), - Cs_ - ( - dimensioned::lookupOrAddToDict - ( - "Cs", - this->coeffDict_, - 0.3 - ) - ), - hh_ - ( - dimensioned::lookupOrAddToDict // This is the relative step height - ( - "hh_", - this->coeffDict_, - 1.0 - ) - ), - nuTilda_ - ( - IOobject - ( - "nuTilda", - this->runTime_.timeName(), - this->mesh_, - IOobject::NO_READ, - IOobject::NO_WRITE - ), - this->mesh_ - ), - y_(wallDist::New(this->mesh_).y()), - // Filtering operations - simpleFilter - MyFilter_ - ( - this->mesh_ - ), - // filterPtr_ - // ( - // LESfilter::New(this->mesh_, this->coeffDict()) - // ), - // filter_ - // ( - // filterPtr_() - // ), - omp_num_threads_(4) - { - if (type == typeName) - { - this->printCoeffs(type); - } - - graph_ = tf_utils::LoadGraph("./ML_SA_CG.pb"); - input_ph_ = {TF_GraphOperationByName(graph_, "input_placeholder"), 0}; - output_ = {TF_GraphOperationByName(graph_, "output_value/BiasAdd"), 0}; - } - -// * * * * * * * * * * * * * * * * Constructing filter * * * * * * * * * * * * * * // -// * * * * * * * * * * * * * * * Member Functions * * * * * * * * * * * * * // - -template -bool ML_SA_CG::read() -{ - if (eddyViscosity>::read()) - { - sigmaNut_.readIfPresent(this->coeffDict()); - kappa_.readIfPresent(this->coeffDict()); - - Cb1_.readIfPresent(this->coeffDict()); - Cb2_.readIfPresent(this->coeffDict()); - Cw1_ = Cb1_/sqr(kappa_) + (1.0 + Cb2_)/sigmaNut_; - Cw2_.readIfPresent(this->coeffDict()); - Cw3_.readIfPresent(this->coeffDict()); - Cv1_.readIfPresent(this->coeffDict()); - Cs_.readIfPresent(this->coeffDict()); - - return true; - } - else - { - return false; - } -} - -template -tmp ML_SA_CG::DnuTildaEff() const -{ - return tmp - ( - new volScalarField("DnuTildaEff", (nuTilda_ + this->nu())/sigmaNut_) - ); -} - - -template -tmp ML_SA_CG::k() const -{ - return tmp - ( - new volScalarField - ( - IOobject - ( - "k", - this->runTime_.timeName(), - this->mesh_ - ), - this->mesh_, - dimensionedScalar("0", dimensionSet(0, 2, -2, 0, 0), 0) - ) - ); -} - - -template -tmp ML_SA_CG::epsilon() const -{ - WarningInFunction - << "Turbulence kinetic energy dissipation rate not defined for " - << "Spalart-Allmaras model. Returning zero field" - << endl; - - return tmp - ( - new volScalarField - ( - IOobject - ( - "epsilon", - this->runTime_.timeName(), - this->mesh_ - ), - this->mesh_, - dimensionedScalar("0", dimensionSet(0, 2, -3, 0, 0), 0) - ) - ); -} - -template -void ML_SA_CG::correct() -{ - if (!this->turbulence_) - { - return; - } - - eddyViscosity>::correct(); - - // Read scaling parameters - RectangularMatrixmean_std_vals(IFstream("means")()); - - int num_inputs = 5; - int num_outputs = 1; - double mean_array [num_inputs+num_outputs] = {0}; - double std_array [num_inputs+num_outputs] = {0}; - - for (int i=0; i<=num_inputs+num_outputs; i++) - { - mean_array[i] = mean_std_vals(0,i); - std_array[i] = mean_std_vals(1,i); - } - - run_ml_graph(mean_array,std_array,num_inputs,num_outputs); -} - -template -void ML_SA_CG::run_ml_graph(double* mean_array, double* std_array, int num_inputs, int num_outputs) -{ - omp_set_num_threads(omp_num_threads_); - - // Structure for tensors in ML - int num_cells = this->mesh_.cells().size(); - - // Velocity related - volScalarField u_ = this->U_.component(vector::X); - volScalarField v_ = this->U_.component(vector::Y); - - - // Datastructure for output - volScalarField nut_ml_ = this->nut_; - // Local references - const alphaField& alpha = this->alpha_; - const rhoField& rho = this->rho_; - const volScalarField chi(this->chi()); - const volScalarField fv1(this->fv1(chi)); - const volScalarField Stilda(this->Stilda(chi, fv1)); - - volScalarField source_term = fvc::Sp(Cw1_*alpha*rho*fw(Stilda)*nuTilda_/sqr(y_), nuTilda_); - - // Some tensorflow pointer requirements - TF_Status* status_ = TF_NewStatus(); - TF_SessionOptions* options_ = TF_NewSessionOptions(); - TF_Session* sess_ = TF_NewSession(graph_, options_, status_); - - float input_vals[num_cells][num_inputs]; - const std::vector input_dims = {num_cells, num_inputs}; - - volScalarField cx_ = this->mesh_.C().component(vector::X); - volScalarField cy_ = this->mesh_.C().component(vector::Y); - - std::cout << "/* Value of relative step height (hh_) is */" << hh_.value() <(TF_TensorData(output_tensors_[0])); - for (int i = 0; i < num_cells; i++) - { - nut_ml_[i] = data[num_outputs*i]*std_array[num_inputs] + mean_array[num_inputs]; // Funnel changes back into OF - row major order - } - - tf_utils::DeleteTensor(input_tensor_); - tf_utils::DeleteTensor(output_tensor_); - TF_DeleteSessionOptions(options_); - TF_DeleteStatus(status_); - tf_utils::DeleteSession(sess_); - - nut_ml_ = MyFilter_(nut_ml_); - - forAll(nut_ml_.internalField(), id) - { - this->nut_[id] = max(nut_ml_[id],0.0); - } - - this->nut_.correctBoundaryConditions(); - fv::options::New(this->mesh_).correct(this->nut_); -} - -// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // - -} // End namespace RASModels -} // End namespace Foam - -// ************************************************************************* // diff --git a/OF_Model/lnInclude/ML_SA_CG.H b/OF_Model/lnInclude/ML_SA_CG.H deleted file mode 100644 index cf81ab6..0000000 --- a/OF_Model/lnInclude/ML_SA_CG.H +++ /dev/null @@ -1,210 +0,0 @@ -/*---------------------------------------------------------------------------*\ - ========= | - \\ / F ield | OpenFOAM: The Open Source CFD Toolbox - \\ / O peration | - \\ / A nd | Copyright (C) 2011-2016 OpenFOAM Foundation - \\/ M anipulation | -------------------------------------------------------------------------------- -License - This file is part of OpenFOAM. - - OpenFOAM is free software: you can redistribute it and/or modify it - under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - OpenFOAM is distributed in the hope that it will be useful, but WITHOUT - ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or - FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License - for more details. - - You should have received a copy of the GNU General Public License - along with OpenFOAM. If not, see . - -Class - Foam::RASModels::ML_SA_CG - -Group - grpRASTurbulence - -Description - Openfoam Machine learning model - Romit Maulik - -SourceFiles - ML_SA_CG.C - -\*---------------------------------------------------------------------------*/ - -#ifndef ML_SA_CG_H -#define ML_SA_CG_H - -#include "RASModel.H" -#include "eddyViscosity.H" -#include "tf_utils.H" // Loads in tensorflow functionality - -// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // - -namespace Foam -{ -namespace RASModels -{ - -/*---------------------------------------------------------------------------*\ - Class ML_SA_CG Declaration -\*---------------------------------------------------------------------------*/ - -template -class ML_SA_CG -: - public eddyViscosity> -{ - // Private Member Functions - - // Disallow default bitwise copy construct and assignment - ML_SA_CG(const ML_SA_CG&); - void operator=(const ML_SA_CG&); - - -protected: - - // Protected data - - // Model coefficients - - dimensionedScalar sigmaNut_; - dimensionedScalar kappa_; - - dimensionedScalar Cb1_; - dimensionedScalar Cb2_; - dimensionedScalar Cw1_; - dimensionedScalar Cw2_; - dimensionedScalar Cw3_; - dimensionedScalar Cv1_; - dimensionedScalar Cs_; - dimensionedScalar hh_; - - // Tensorflow graph operations - protected members of this class - top three not used in constructor definition since they don't have default values - TF_Graph* graph_; - TF_Output input_ph_; - TF_Output output_; - - // Fields - volScalarField nuTilda_; - // volScalarField umag_; - // volScalarField gradu_; - // volScalarField gradv_; - // volScalarField rot_; - // volScalarField vortz_; - // volScalarField nut_ml_; - - //- Wall distance - // Note: different to wall distance in parent RASModel - // which is for near-wall cells only - const volScalarField& y_; - - // LES filters - simpleFilter MyFilter_; - // autoPtr filterPtr_; - // LESfilter& filter_; - - // OpenMP num threads - int omp_num_threads_; - - - - - // Protected Member Functions - - tmp chi() const; - - tmp fv1(const volScalarField& chi) const; - - tmp fv2 - ( - const volScalarField& chi, - const volScalarField& fv1 - ) const; - - tmp Stilda - ( - const volScalarField& chi, - const volScalarField& fv1 - ) const; - - tmp fw(const volScalarField& Stilda) const; - - void correctNut(const volScalarField& fv1); - virtual void correctNut(); - - //- run the ML graph - void run_ml_graph(double* mean_array, double* std_array, int num_inputs, int num_outputs); - - -public: - - typedef typename BasicTurbulenceModel::alphaField alphaField; - typedef typename BasicTurbulenceModel::rhoField rhoField; - typedef typename BasicTurbulenceModel::transportModel transportModel; - - - //- Runtime type information - TypeName("ML_SA_CG"); - - // Constructors - - //- Construct from components - ML_SA_CG - ( - const alphaField& alpha, - const rhoField& rho, - const volVectorField& U, - const surfaceScalarField& alphaRhoPhi, - const surfaceScalarField& phi, - const transportModel& transport, - const word& propertiesName = turbulenceModel::propertiesName, - const word& type = typeName, - int omp_num_threads_ = 4 - ); - - - //- Destructor - - virtual ~ML_SA_CG() - { - } - - - // Member Functions - - //- Read RASProperties dictionary - virtual bool read(); - - //- Return the effective diffusivity for nuTilda - tmp DnuTildaEff() const; - - //- Return the turbulence kinetic energy - virtual tmp k() const; - - //- Return the turbulence kinetic energy dissipation rate - virtual tmp epsilon() const; - - //- Solve the turbulence equations and correct the turbulence viscosity - virtual void correct(); -}; - - -// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // - -} // End namespace RASModels -} // End namespace Foam - -// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // - -#ifdef NoRepository - #include "ML_SA_CG.C" -#endif - -// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * // - -#endif - -// ************************************************************************* // diff --git a/README.md b/README.md index 0923c5e..316a9dc 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ to obtain the following output ``` Hello from TensorFlow C library version 1.15.0 ``` -If you have reached this point - congratulations you are ready to use TensorFlow 1.15 *within* OpenFOAM 5. +If you have reached this point - congratulations you are ready to use TensorFlow 1.15 *within* OpenFOAM 5. You may utilize the individual READMEs from `ML_RANS/` and `ML_LES` (the latter in-progress) to construct a neural network based turbulence model for using in OpenFOAM. ## Step 3: Train a model in Python diff --git a/Training/PDF_Plots.ipynb b/Training/PDF_Plots.ipynb deleted file mode 100644 index 4cc2c3d..0000000 --- a/Training/PDF_Plots.ipynb +++ /dev/null @@ -1,386 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cairo\n", - "['sans-serif']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rmlans/anaconda3/envs/deephyper_env/lib/python3.6/site-packages/ipykernel_launcher.py:16: MatplotlibDeprecationWarning: \n", - "The text.latex.unicode rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\n", - " app.launch_new_instance()\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "%matplotlib inline\n", - "import matplotlib.pylab as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import re\n", - "import matplotlib as mpl\n", - "import matplotlib.font_manager as fm\n", - "from matplotlib import pyplot as plt\n", - "\n", - "\n", - "mpl.rcParams['ps.useafm'] = True\n", - "#mpl.rcParams['pdf.use14corefonts'] = True\n", - "mpl.rcParams['text.usetex'] = True\n", - "mpl.rc('text', usetex=True) \n", - "mpl.rcParams['text.latex.unicode']=True\n", - "mpl.use( \"cairo\", warn=False, force=True)\n", - "print(mpl.pyplot.get_backend())\n", - "\n", - "mpl.rcParams['text.latex.preamble'] = [\n", - " r'\\usepackage{siunitx}', # i need upright \\micro symbols, but you need...\n", - " r'\\sisetup{detect-all}', # ...this to force siunitx to actually use your fonts\n", - " r'\\usepackage[T1]{fontenc}',\n", - " r'\\usepackage[default]{gillius}', # set the normal font here\n", - " r'\\usepackage{sansmath}', # load up the sansmath so that math -> helvet\n", - " r'\\sansmath' # <- tricky! -- gotta actually tell tex to use!\n", - "] \n", - "\n", - "\n", - "def init_plotting():\n", - " plt.rcParams['figure.figsize'] = (8, 5)\n", - " plt.rcParams['font.size'] = 20\n", - " print(plt.rcParams['font.family'] )\n", - " plt.rcParams['axes.labelsize'] = plt.rcParams['font.size']\n", - " plt.rcParams['axes.titlesize'] = plt.rcParams['font.size']\n", - " plt.rcParams['legend.fontsize'] = 0.9*plt.rcParams['font.size']\n", - " plt.rcParams['xtick.labelsize'] = plt.rcParams['font.size']\n", - " plt.rcParams['ytick.labelsize'] = plt.rcParams['font.size']\n", - "# print(plt.rcParams['savefig.dpi'])\n", - "# plt.rcParams['savefig.dpi'] = 2*plt.rcParams['savefig.dpi']\n", - " plt.rcParams['xtick.minor.visible']=False\n", - " plt.rcParams['ytick.minor.visible']=False\n", - " plt.rcParams['xtick.major.size'] = 6\n", - " plt.rcParams['xtick.minor.size'] = 3\n", - " plt.rcParams['xtick.major.width'] = 2\n", - " plt.rcParams['xtick.minor.width'] = 2\n", - " plt.rcParams['ytick.major.size'] = 6\n", - " plt.rcParams['ytick.minor.size'] = 3\n", - " plt.rcParams['ytick.major.width'] = 2\n", - " plt.rcParams['ytick.minor.width'] = 2\n", - " plt.rcParams['ytick.color'] = \"black\"#\"#808080\"\n", - " plt.rcParams['xtick.color'] = \"black\"#\"#808080\"\n", - " plt.rcParams['xtick.direction'] = 'in'\n", - " plt.rcParams['ytick.direction'] = 'in'\n", - " #ax.tick_params(direction='out', length=6, width=2, colors='r',\n", - " # grid_color='r', grid_alpha=0.5)\n", - " plt.rcParams['legend.frameon'] = False\n", - " plt.rcParams['legend.loc'] = 'best'\n", - " plt.rcParams['axes.linewidth'] = 2\n", - "\n", - "init_plotting()\n", - "\n", - "\n", - "\n", - "def hide_spines():\n", - " \"\"\"Hides the top and rightmost axis spines from view for all active\n", - " figures and their respective axes.\"\"\"\n", - " # Retrieve a list of all current figures.\n", - " figures = [x for x in mpl._pylab_helpers.Gcf.get_all_fig_managers()]\n", - " for figure in figures:\n", - " # Get all Axis instances related to the figure.\n", - " for ax in figure.canvas.figure.get_axes():\n", - " \n", - " ax.spines['left'].set_color(\"#808080\")\n", - " ax.spines['bottom'].set_color(\"#808080\")\n", - " ax.spines['right'].set_color(\"#808080\")\n", - " ax.spines['top'].set_color(\"#808080\")\n", - " ax.xaxis.set_ticks_position('bottom')\n", - " ax.yaxis.set_ticks_position('left')\n", - " \n", - " ax.spines['top'].set_linewidth(0.1)\n", - " ax.spines['top'].set_linestyle(':')\n", - " ax.spines['right'].set_linewidth(0.1)\n", - " ax.spines['right'].set_linestyle(':')\n", - "\n", - "\n", - "hide_spines()\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "current_palette = sns.color_palette()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rmlans/anaconda3/envs/deephyper_env/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", - " warnings.warn(msg, category=DeprecationWarning)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import tensorflow as tf\n", - "from sklearn.externals import joblib\n", - "# Fixing paths\n", - "import os, sys\n", - "HERE = os.getcwd()\n", - "PARENT = os.path.dirname(os.getcwd())\n", - "ML_DATA_DIR = PARENT+'/Field_Reader/'\n", - "sys.path.insert(0,HERE)\n", - "\n", - "def load_graph(frozen_graph_filename):\n", - " # We load the protobuf file from the disk and parse it to retrieve the \n", - " # unserialized graph_def\n", - " with tf.gfile.GFile(frozen_graph_filename, \"rb\") as f:\n", - " graph_def = tf.GraphDef()\n", - " graph_def.ParseFromString(f.read())\n", - "\n", - " # Then, we import the graph_def into a new Graph and returns it \n", - " with tf.Graph().as_default() as graph:\n", - " # The name var will prefix every op/nodes in your graph\n", - " # Since we load everything in a new graph, this is not needed\n", - " tf.import_graph_def(graph_def, name=\"prefix\")\n", - " return graph" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training assessment file\n", - "[ 4.07968692e+01 -1.20824351e+00 -3.26113973e-01 5.64016389e-02\n", - " 1.38270662e+00 1.61369499e-03]\n", - "[4.78414239e+01 2.93056339e+01 3.09706634e-01 1.68978913e-03\n", - " 3.03334714e-01 5.15068686e-06]\n" - ] - } - ], - "source": [ - "print('Training assessment file')\n", - "num_inputs = 5\n", - "\n", - "# Load data\n", - "total_data = np.load(ML_DATA_DIR+'Total_dataset.npy')\n", - "true_op = np.copy(total_data[:,num_inputs:])\n", - "# We use our \"load_graph\" function\n", - "graph = load_graph('ML_SA_CG.pb')\n", - "# We access the input and output nodes \n", - "x = graph.get_tensor_by_name('prefix/input_placeholder:0')\n", - "y = graph.get_tensor_by_name('prefix/output_value/BiasAdd:0')\n", - "\n", - "# Scale features\n", - "scaler_filename = \"mv_scaler.save\"\n", - "scaler = joblib.load(scaler_filename)\n", - "print(scaler.mean_)\n", - "print(scaler.var_)\n", - "\n", - "total_data[:,:] = scaler.transform(total_data[:,:])\n", - "true_transformed = np.copy(total_data[:,num_inputs:])\n", - "\n", - "# We launch a Session\n", - "with tf.Session(graph=graph) as sess:\n", - " # Note: we don't nee to initialize/restore anything\n", - " # There is no Variables in this graph, only hardcoded constants \n", - " y_out = sess.run(y, feed_dict={\n", - " x: total_data[:,:num_inputs]\n", - " })\n", - "\n", - "# # Scatter plot \n", - "# Rescale y_out to be in physical domain\n", - "total_data[:,num_inputs:] = y_out[:,:]\n", - "total_data[:,:] = scaler.inverse_transform(total_data[:,:])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "x=total_data[:,-1].flatten()\n", - "y=true_op[:,0].flatten()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['sans-serif']\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(7,6))\n", - "sns.distplot(x,kde=False,color=\"red\",label='Predicted',hist_kws=dict(alpha=0.5),norm_hist=True)\n", - "sns.distplot(y,kde=False,color=\"black\",label='True',hist_kws=dict(histtype=\"step\",alpha=1.0,linewidth=3),norm_hist=True)\n", - "plt.legend(frameon=False)\n", - "plt.xlabel(r\"$\\nu_t$\")\n", - "plt.ylabel(\"Density\")\n", - "init_plotting()\n", - "plt.tight_layout()\n", - "\n", - "# hide_spines()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rmlans/anaconda3/envs/deephyper_env/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " if __name__ == '__main__':\n", - "/home/rmlans/anaconda3/envs/deephyper_env/lib/python3.6/site-packages/IPython/core/pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Scatter plot \n", - "plt.figure(figsize=(7,6))\n", - "# plt.scatter(true_op[:,0].flatten(),total_data[:,-1].flatten(),label='ML',c=wall_distance,cmap=cm.YlOrBr,alpha=0.5)\n", - "plt.plot(true_op[:,0].flatten(),total_data[:,-1].flatten(),'ro',label='Predicted',markersize=3,alpha=0.4)\n", - "plt.plot(true_op[:,0].flatten(),true_op[:,0].flatten(),'k-',linestyle='--',label='Truth') # identity line\n", - "plt.xlabel('Truth')\n", - "plt.ylabel('Predicted')\n", - "lgnd = plt.legend(markerscale=3)\n", - "plt.tight_layout()\n", - "\n", - "\n", - "plt.xlim((true_op[:,0].min(),true_op[:,0].max()))\n", - "plt.ylim((true_op[:,0].min(),true_op[:,0].max()))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "training_data = np.loadtxt('training.log',skiprows=1,delimiter=',')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(14,6),nrows=1,ncols=2)\n", - "ax[0].plot(training_data[:,0],training_data[:,1],label='Training',linewidth=3)\n", - "ax[0].plot(training_data[:,0],training_data[:,3],label='Validation',linewidth=3)\n", - "ax[0].set_xlabel('Epoch')\n", - "ax[0].set_ylabel('Mean-squared error')\n", - "\n", - "ax[1].plot(training_data[:,0],training_data[:,2],label='Training',linewidth=3)\n", - "ax[1].plot(training_data[:,0],training_data[:,4],label='Validation',linewidth=3)\n", - "ax[1].set_xlabel('Epoch')\n", - "ax[1].set_ylabel('$R^2$')\n", - "\n", - "plt.tight_layout()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}