CFD_collocated.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CFD from scratch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Governing equations\n",
    "* Pressure\n",
    "* Finite volume formulation\n",
    "* Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Governing Equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reynolds transport theorem (RTT)\n",
    "\n",
    "* The RTT connects a Lagrangian system with an Eulerian control volume. \n",
    "    * By Lagrangian system, we mean a system of a given specified mass, or a *marked mass*.\n",
    "* Conservation laws are written in terms of Lagrangian systems, but we solve CFD problems on Eulerian domains. The RTT connects these.\n",
    "\n",
    "<img src=\"rtt.png\" width=\"350\"/>\n",
    "\n",
    "* The RTT is written as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\underbrace{\\frac{dB_{sys}}{dt}}_{\\text{Lagrangian system}} = \\underbrace{\\frac{d}{dt}\\int_V\\rho\\beta dV + \\int_A\\rho\\beta\\vec{v}\\cdot\\vec{n}dA}_{\\text{Eulerian control volume}}.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* $B_{sys}$ is some extensive quantity, like mass, momentum, or energy.\n",
    "* $\\beta = B_{sys}/m$ is intensive.\n",
    "* $\\rho\\beta$ will be mass, momentum, energy, etc. per unit volume.\n",
    "* $\\rho\\beta\\vec{v}$ is $B$-flux, like mass flux, momentum flux, energy flux.\n",
    "* $\\vec{n}$ is a unit normal vector pointing *out* of the surface of a control volume. \n",
    "* In an area dA, $\\rho\\beta\\vec{v}\\cdot\\vec{n}dA$ will be rate of B flowing out of a control volume through that area dA. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mass\n",
    "\n",
    "* $B_{sys} = m$\n",
    "* $\\beta = B_{sys}/m = 1$.\n",
    "* Lagrangian conservation law: mass is conserved, or the rate of change of a given mass is zero:\n",
    "$$\\frac{dm}{dt} = 0.$$\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho dV + \\int_A\\rho\\vec{v}\\cdot\\vec{n}dA = 0.$$\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum\n",
    "* $B_{sys} = m\\vec{v}$\n",
    "* $\\beta = B_{sys}/m = \\vec{v}$\n",
    "* Lagrangian conservation law: the rate of change of momentum of a fixed mass (system) is the sum of the external forces on the mass (system).\n",
    "    * We have surface forces $\\vec{F}$, and body forces (denoted with external field $\\vec{g}$, nominally gravitational acceleration):\n",
    "$$\\frac{dm\\vec{v}}{dt} = \\int_A \\vec{F}dA + \\int_V\\vec{g}\\rho dV.$$\n",
    "    * Consider viscous and pressure forces, so that $\\vec{F} = -\\boldsymbol{\\tau}\\cdot\\vec{n} - P\\boldsymbol{\\delta}\\cdot{\\vec{n}}$, where $\\boldsymbol{\\tau}$ is the viscous stress tensor, and $\\boldsymbol{\\delta}$ is the unit tensor. \n",
    "        * (The negative sign is because $\\vec{n}$ points *out* of the suface, and we want the force on or into the surface.) \n",
    "    * This gives\n",
    "$$\\frac{dm\\vec{v}}{dt} = -\\int_A\\boldsymbol{\\tau}\\cdot\\vec{n}dA -\\int_AP\\boldsymbol{\\delta}\\cdot\\vec{n}dA+ \\int_V\\vec{g}\\rho dV.$$\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho\\vec{v}dV + \\int_A\\rho\\vec{v}\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\boldsymbol{\\tau}\\cdot\\vec{n}dA -\\int_AP\\boldsymbol{\\delta}\\cdot\\vec{n}dA+ \\int_V\\vec{g}\\rho dV.$$\n",
    "</font>\n",
    "* Here, $\\vec{v}\\vec{v}$ is a tensor, and can be written as $\\vec{v}\\otimes\\vec{v}$, or $v_iv_j$ in index notation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy\n",
    "* $B_{sys} = E$ (where $E = mu + \\frac{1}{2}m\\vec{v}\\cdot\\vec{v}$ is internal + kinetic energy).\n",
    "* $\\beta = E/m = e$.\n",
    "* Lagrangian conservation law: the rate of change of energy of a given mass is the sum of the heat transfered to the mass and the work performed on the mass:\n",
    "$$\\frac{dE}{dt} = -\\int_A\\vec{q}\\cdot\\vec{n}dA + \\int_A\\vec{F}\\cdot\\vec{v}dA + \\int_V\\rho\\vec{g}\\cdot\\vec{v}dV.$$\n",
    "    * Here, $\\vec{q}$ is the heat flux vector. As before, $\\vec{F} = -\\boldsymbol{\\tau}\\cdot\\vec{n} - P\\boldsymbol{\\delta}\\cdot{\\vec{n}}$. \n",
    "    * The symmetry of $\\boldsymbol{\\tau}$ and $\\boldsymbol{\\delta}$ let us write $\\boldsymbol{\\tau}\\cdot\\vec{n}\\cdot\\vec{v} = \\boldsymbol{\\tau}\\cdot\\vec{v}\\cdot\\vec{n}$ and $P\\boldsymbol{\\delta}\\cdot\\vec{n}\\cdot\\vec{v} = P\\boldsymbol{\\delta}\\cdot\\vec{v}\\cdot\\vec{n}$.\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho edV + \\int_A\\rho e\\vec{v}\\cdot\\vec{n}dA = \n",
    "-\\int_A\\vec{q}\\cdot\\vec{n}dA - \\int_A(\\boldsymbol{\\tau}\\cdot\\vec{v})\\cdot\\vec{n}dA - \\int_A(P\\boldsymbol{\\delta}\\cdot\\vec{v})\\cdot\\vec{n}dA+ \\int_V\\rho\\vec{g}\\cdot\\vec{v}dV.$$\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equation of state and constitutive relations\n",
    "* The above (blue) equations for mass, momentum and energy are three equations in $\\rho$, $\\vec{v}$ and $e$.\n",
    "* An equation of state is used to relate these quantities to pressure $P$.\n",
    "    * For, example, the ideal gas law:\n",
    "$$P = \\frac{\\rho R T}{M}$$\n",
    "    * Here, $R$ is the universal gas constant, $M$ is the mean molecular weight, and $T$ is temperature.\n",
    "* Thermodynamic relations will relate $T$ to $e$, $\\vec{v}$, and $P$. For ideal gases, $T$ is a function of internal energy $e-\\frac{1}{2}\\vec{v}\\cdot\\vec{v}$.\n",
    "* Consitutive relations are also required to write $\\vec{q}$, and $\\boldsymbol{\\tau}$ in terms of $\\rho$, $\\vec{v}$, $e$, $P$."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differential form\n",
    "\n",
    "* The above equations are in integral form, which is convenient for a Finite Volume solution. (It is also convenient for derivation.)\n",
    "* We can find the differential form as follows.\n",
    "    * If the control volume is fixed in time, we can move $d/dt$ inside the volume integral.\n",
    "    * Replace integrals over the surface area with volume integrals by applying the Gauss Divergence Theorem:\n",
    "    $$\\int_A\\vec{v}\\cdot\\vec{n}dA = \\int_V\\nabla\\cdot\\vec{v},$$\n",
    "    where $\\vec{v}$ is some vector (not necessarily velocity).\n",
    "        * Hence, in the equation for mass we have $\\int_A\\rho\\vec{v}\\cdot\\vec{n}dA = \\int_V\\nabla\\cdot(\\rho\\vec{v})dV$.\n",
    "    * Combine all the volume integrals into $\\int_V(\\text{all terms})dV = 0$. Since the volume integrated over is arbitrary, this equation can only be true if the integrand $(\\text{all terms})$ itself is 0. This gives the final result. (Also, $\\nabla\\cdot(P\\boldsymbol{\\delta}) = \\nabla P$.)\n",
    "    \n",
    "\\begin{align}\n",
    "\\text{mass:} \\phantom{xxxx}& \\frac{d\\rho}{dt} + \\nabla\\cdot(\\rho\\vec{v}) = 0\\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{d\\rho\\vec{v}}{dt} + \\nabla\\cdot(\\rho\\vec{v}\\vec{v}) = -\\underbrace{\\nabla\\cdot\\boldsymbol{\\tau}}_{\\text{viscous forces}} - \\underbrace{\\nabla P}_{\\text{pressure forces}} + \\underbrace{\\rho\\vec{g}}_{\\text{gravitational forces}} \\\\\n",
    "\\text{energy:}\\phantom{xxxx} & \\frac{d\\rho e}{dt} + \\nabla\\cdot(\\rho e\\vec{v}) = -\\underbrace{\\nabla\\cdot\\vec{q}}_{\\text{heat flux}} - \\underbrace{\\nabla\\cdot(\\boldsymbol{\\tau}\\cdot\\vec{v})}_{\\text{viscous heating}} -\\underbrace{\\nabla\\cdot(P\\vec{v})}_{\\text{PV work}} + \\underbrace{\\rho\\vec{g}\\cdot\\vec{v}}_{\\text{field work}}\n",
    "\\end{align}\n",
    "\n",
    "* The terms on the left hand side (LHS) of the equation are the accumulation and in/out transport through the control volume.\n",
    "* The terms on the right hand side (RHS) are as noted. In the energy equation, field work will convert potential energy to kinetic energy (which is part of $e$)."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pressure and compressibility\n",
    "* The governing equations, along with equation of state and constitutive, define the system. \n",
    "    * Oh, and boundary conditions too, don't forget those.\n",
    "* These are the compressible flow equations. \n",
    "* Pressure waves will be part of the solution.\n",
    "* Those waves travel at the sound speed (347 m/s in at at 1 atm, 300 K). \n",
    "* If we are at low speed relative to the acoustic waves (low Mach), then the flow is largely unimpacted by these waves and we call the flow incompressible.\n",
    "    * From the Bernoulli equation, ignoring gravity, we have\n",
    "    $$\\Delta P = \\frac{\\rho\\Delta v^2}{2}.$$\n",
    "    * Take pressures relative to stagnation, so $\\Delta v=v$ (most flows have $v=0$ somewhere). Using $c^2=\\gamma P/\\rho$, where $\\gamma = c_p/c_v$, and $Ma = v/c$, we have\n",
    "    $$\\frac{\\Delta P}{P} = \\frac{\\gamma}{2}Ma^2.$$\n",
    "    * For v=100 m/s, $\\Delta P/P_{atm}$ is 6%. (100 m/s is 233 mph and gives Ma=0.288 for air at 1 atm.)\n",
    "    * For v=347 m/s, $\\Delta P/P_{atm}$ is 70%. (347 m/s is 776 mph and gives Ma=1 for air at 1 atm.)\n",
    "* Flows are often considered incompressible for $Ma<0.3$.\n",
    "* Flows can have large density changes and still be considered incompressible. Heat release and chemical mixing in gases can cause large density variations, but at low speed, the changes in velocity themselves only slightly compress the fluid."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### Low-Mach flows\n",
    "* At low speeds, acoustic waves do not alter the thermodynamic state or affect the velocity fields. \n",
    "* But for unsteady, explicit flow solvers, we are required to take timesteps based on the smallest timescales. Here, those correspond to acoustic waves, or rather, $v+c$. \n",
    "* This is a numerically *stiff* problem: we have to take timesteps for stability that are much smaller than we need for accuracy (for capturing the relevant physics, of which acoustics don't contribute.)\n",
    "    * The ratio of the number of timesteps needed for stability to the ideal number for accuracy is $(v+c)/v = 1+1/Ma$. \n",
    "        * This is based on a CFL condition: accurate timestep sizes will be smaller than the convective time across a given grid cell: $\\Delta t < \\alpha\\Delta x/v$ for accuracy, but $\\Delta t < \\alpha\\Delta x/(v+c)$ for stability, where $\\alpha$ is a constant (the CFL) determined by the method used. \n",
    "    * At Ma=0.1 (34.7 m/s or 77.6 mph in air at 1 atm, 300 K), we would need 11 times more steps for a stable solution than we need for an acurate solution. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incompressible flow\n",
    "* The numerical stiffness for low-Mach flows can be explicitly removed from the equations by assuming the flow is incompressible and using a so-called pressure projection method.\n",
    "* The density is assumed constant, and the energy equation is no longer needed.\n",
    "* The resulting equations, ignoring gravity, and taking $\\hat{\\tau}=\\tau/\\rho$, and $\\hat{P} = P/\\rho$ are\n",
    "<font color=\"blue\">\n",
    "\\begin{align}\n",
    "\\text{mass:}\\phantom{xxxx} & \\int_A\\vec{v}\\cdot\\vec{n}dA = 0 \\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{d}{dt}\\int_V\\vec{v}dV + \\int_A\\vec{v}\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}\\cdot\\vec{n}dA -\\int_A\\hat{P}\\boldsymbol{\\delta}\\cdot\\vec{n}dA.\n",
    "\\end{align}\n",
    "</font>\n",
    "* These are two equations in $\\vec{v}$ and $P$. \n",
    "* The momentum equation gives the equation for $\\vec{v}$, but there is no explicit pressure equation. The mass equation (continuity) provides a constraint on the velocities.\n",
    "* Pressure can be thought of as a scalar field that constrains velocity components given by the momentum equation to satisfy continuity.\n",
    "* A pressure equation is derived by effectively taking the divergence of the momentum equation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pressure equation\n",
    "* Consider the differential form of the mass and momentum equations, where we drop the vector arrows for simplicity:\n",
    "\\begin{align}\n",
    "\\text{mass:}\\phantom{xxxx} & \\nabla\\cdot \\vec{v} = 0 \\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{d\\vec{v}}{dt} = \\underbrace{-\\nabla\\cdot \\vec{v}\\vec{v} - \\nabla\\cdot\\hat{\\boldsymbol{\\tau}}}_{H} - \\nabla\\hat{P}\n",
    "\\end{align}\n",
    "* Apply a simple Explicit Euler integration over one timestep $\\Delta t$ to the momentum equation:\n",
    "$$\\vec{v}^{n+1} = \\vec{v}^n + \\Delta tH^n-\\Delta t\\nabla\\hat{P}^{n+1}.$$\n",
    "    * Note that $\\hat{P}$ is taken at step $n+1$ (keep reading).\n",
    "* We use $\\nabla\\cdot \\vec{v}=0$ applied to this $\\vec{v}^{n+1}$ equation to get an equation for $\\hat{P}^{n+1}$:\n",
    "<font color=\"blue\">\n",
    "$$\\nabla^2\\hat{P}^{n+1} = \\nabla\\cdot H^n.$$\n",
    "</font>\n",
    "* **To advance a given step, we solve this equation for $\\hat{P}$, and then use this in the momentum advancement equation for $\\vec{v}^{n+1}$ above:**\n",
    "<font color=\"blue\">\n",
    "$$\\vec{v}^{n+1} = \\vec{v}^n + \\Delta tH^n - \\Delta t\\nabla\\hat{P}^{n+1}.$$\n",
    "</font>\n",
    "* This velocity satisfies continuity by construction.\n",
    "\n",
    "**In practice, the pressure equation is determined from the discretized version of the momentum and continuity equations, discussed later.**\n",
    "\n",
    "#### Boundary conditions for the pressure equation\n",
    "* The pressure equation requires boundary conditions. These are best determined from the momentum equations evaluated on the boundary. \n",
    "* There appears to be ongoing discussion of the boundary condition treatment for the pressure equation. See for example \n",
    "    * [Vremen 2014](https://linkinghub.elsevier.com/retrieve/pii/S002199911400062X)\n",
    "    * [Rempfer 2003](https://www.annualreviews.org/doi/abs/10.1146/annurev.fluid.35.030602.113908?journalCode=fluid)\n",
    "    * [Sani 2006](https://onlinelibrary.wiley.com/doi/10.1002/fld.1062)\n",
    "        * Along with [some comentantary by Rempfer](https://onlinelibrary.wiley.com/doi/pdf/10.1002/fld.1750)\n",
    "        * And a [reply by Sani](https://onlinelibrary.wiley.com/doi/pdf/10.1002/fld.1762)\n",
    "* In my opinion, the pressure treatment is not so complicated. The simplicity of inserting the velocities into the continuity equation, with boundary velocities taken along for the ride, is pretty straightforward. Pressure boundary conditions are effectively built-in. See below.        \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Solution-Finite Volume Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To solve the above equations, we'll use a finite volume method. \n",
    "* Assume a 2-dimensional rectangular domain. \n",
    "    * We divide the domain into grid cells of size $\\Delta x$ by $\\Delta y$. \n",
    "    * $\\Delta x\\Delta y$ is the volume of a grid cell.\n",
    "    * Cell face areas are $\\Delta x$ and $\\Delta y$.\n",
    "<img src=\"fig_1.png\" width=\"250\"/>\n",
    "    * cell faces are labeled as e, w, n, s for east, west, north, and south, respectively.\n",
    "    * The given cell is denoted cell P, and neighboring cells are N, S, E, and W.\n",
    "    * Cells are indexed with $i$ in the x direction and $j$ in the y direction, so that cell P is $(i,j)$, E is $(i+1,j)$, W is $(i-1,j)$, N is $(i,j+1)$, and S is $(i,j-1)$.\n",
    "    * The x-direction (horizontal) is indexed with *i* and the y direction (vertical) is indexed with $j$.\n",
    "* We assume all properties are uniform inside a given cell, and we assume all properties are uniform along a given face.\n",
    "    * With these two assumptions, we integrate the mass and momentum equations over a given cell."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum components\n",
    "\\begin{align}\n",
    "\\text{u-mom:}\\phantom{xxxx}&\\frac{d}{dt}\\int_VudV + \\int_Au\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}_x\\cdot\\vec{n}dA -\\int_A\\hat{P}\\vec{i}\\cdot\\vec{n}dA, \\\\\n",
    "\\text{v-mom:}\\phantom{xxxx}&\\frac{d}{dt}\\int_VvdV + \\int_Av\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}_y\\cdot\\vec{n}dA -\\int_A\\hat{P}\\vec{j}\\cdot\\vec{n}dA.\n",
    "\\end{align}\n",
    "Here, $\\vec{i}$ and $\\vec{j}$ are the unit coordinate vectors in the $x$ and $y$ directions, respectively."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Integrate the u-mom equation over a given grid cell.\n",
    "* The first term is\n",
    "$$\\frac{d}{dt}\\int_VudV \\rightarrow \\frac{d}{dt}u\\int_VdV = \\frac{duV}{dt} = \\frac{du\\Delta x\\Delta y}{dt} = \\Delta x\\Delta y\\frac{du}{dt}.$$\n",
    "* The area integrals are illustrated considering the second term, where $\\vec{n}=\\pm\\vec{i}$ or $\\vec{n}=\\pm\\vec{j}$:\n",
    "\n",
    "\\begin{align}\n",
    "\\int_Au\\vec{v}\\cdot\\vec{n}dA &= \n",
    "\\int_{A_e}u\\vec{v}\\cdot\\vec{i}dA - \n",
    "\\int_{A_w}u\\vec{v}\\cdot\\vec{i}dA + \n",
    "\\int_{A_n}u\\vec{v}\\cdot\\vec{j}dA - \n",
    "\\int_{A_s}u\\vec{v}\\cdot\\vec{j}dA \\\\\n",
    "&= u_eu_e\\Delta y - u_wu_w\\Delta y + u_nv_n\\Delta x - u_sv_s\\Delta x.\n",
    "\\end{align}\n",
    "\n",
    "* Considering all terms in the u-mom equation and dividing through by $\\Delta x\\Delta y$ gives\n",
    "\n",
    "$$\\frac{du}{dt} = -\\frac{u_eu_e - u_wu_w}{\\Delta x} - \\frac{u_nv_n - u_sv_s}{\\Delta y}\n",
    "- \\frac{\\hat{\\tau}_{xx,e}-\\hat{\\tau}_{xx,w}}{\\Delta x} \n",
    "- \\frac{\\hat{\\tau}_{xy,n}-\\hat{\\tau}_{xy,s}}{\\Delta y}\n",
    "- \\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x}$$\n",
    "\n",
    "* The quantities at faces interpolated between values at neighboring cells.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkerboarding\n",
    "* Consider in particular the face pressures:\n",
    "$$\\hat{P}_e = \\frac{\\hat{P}_E+\\hat{P}_P}{2},$$\n",
    "$$\\hat{P}_w = \\frac{\\hat{P}_P+\\hat{P}_W}{2}.$$\n",
    "* Inserting these into the u-mom equation gives:\n",
    "$$-\\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x} = -\\frac{\\hat{P}_E+\\hat{P}_P-\\hat{P}_P-\\hat{P}_W}{2\\Delta x} = -\\frac{\\hat{P}_E-\\hat{P}_W}{2\\Delta x}.$$\n",
    "* Note that for the given cell, $u$ depends on the two neighboring pressures, but not on $\\hat{P}_P$.\n",
    "* This effectively decouples $u$ from $\\hat{P}$.\n",
    "* Consider a one-dimensional grid with the following pressure values\n",
    "\n",
    "```\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "```\n",
    "\n",
    "* For any given cell, pressure term will cancel since (8-8)=0 and (2-2)=0. Hence, this pressure field will not affect the velocity. \n",
    "* This is clearly unphysical since there is an alternative pressure gradient that should drive velocity.\n",
    "* In two dimensions, we could have something like:\n",
    "\n",
    "```\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  6  |  4  |  6  |  4  |  6  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  6  |  4  |  6  |  4  |  6  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "```\n",
    "\n",
    "* The decoupling of velocity and pressure can cause instability. Numerical error/noise can be preserved and amplified.\n",
    "* To remedy this, one approach is to use a **staggered grid**.\n",
    "    * A u-grid (and a v-grid) is formed by shifting all grid cells left (or down for v) by half a cell.\n",
    "    * Momentum equations are then integrated over the respective shifted cells.\n",
    "    * Pressures are stored at the original grid and the continuity equation is integrated over the P-cells.\n",
    "    \n",
    "<img src=\"fig_3.png\" width=\"100\"/>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum interpolation\n",
    "* The use of a staggered grid can be inconvenient for complex geometries and unstructured meshes. \n",
    "* An alternative is to use so-called **momentum interpolation**.\n",
    "* A collocated grid is used here. That is, only the P-grid is considered.\n",
    "* In the finite volume formulation, only face velocities appear.\n",
    "* Instead of interpolating these face velocities using, e.g., a standard linear interpolation, the face velocities are computed *as if* they were on a staggered grid.\n",
    "* The momentum equation was written above as\n",
    "$$\\vec{v}^{n+1} = \\vec{v}^n + \\Delta tH^n -\\Delta t\\nabla\\hat{P}^{n+1},$$\n",
    "$$H^n = \\left(-\\nabla\\cdot \\vec{v}\\vec{v}-\\nabla\\cdot\\hat{\\boldsymbol{\\tau}}\\right)^n.$$\n",
    "* Consider the $u$ momentum at a cell P and an adjacent cell E:\n",
    "$$u_P^{n+1} = u_P^n + \\Delta tH_P^{u,n} -\\Delta t(\\nabla\\hat{P})_P^{n+1},$$\n",
    "$$u_E^{n+1} = u_E^n + \\Delta tH_E^{u,n} -\\Delta t(\\nabla\\hat{P})_E^{n+1},$$\n",
    "* In the MWI, a face velocity like $u_e$ is evaluated by *analogy* to the above equations:\n",
    "<font color=\"blue\">\n",
    "$$u_e^{n+1} = u_e^n + \\Delta tH_e^{u,n} -\\Delta t(\\nabla\\hat{P})_e^{n+1}.$$\n",
    "</font>\n",
    "* On a staggered grid, $H_e$ would be computed by integrating over a staggered grid cell. In the MWI, $H_e$ is computed by linear interpolation of the neighboring cell-centered $H$ values, e.g.,\n",
    "<font color=\"blue\">\n",
    "$$H_e^{u} = \\frac{1}{2}(H_P^u + H_E^u).$$\n",
    "</font>\n",
    "* **Note, in the MWI formulation presented here, only face velocities are needed, despite the implication suggested by the term *collocated*. The equation for the face velocities is computed by interpolation of momentum equations integrated over the collocated grid cell.**\n",
    "    * The face velocities carried and solved for a given cell (and shared between adjacent cells) are $u_e$, $u_w$, $v_n$, and $v_s$.\n",
    "    * The finite volume formulation below also requires auxiliary face values $u_n$, $u_s$, $v_e$, and $v_w$. These quantities are computed by standard interpolation among the carried face velocities. \n",
    "    * In addition, some quantities, such velocity gradients appearing in the viscous stress tensor are conveniently computed using cell centered velocities. Such quantities are again evaluated by standard interpolation among the carried face velocities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum equations\n",
    "\n",
    "* Here we present the equations discussed above, using the finite volume formulation.\n",
    "* (Be careful not to confuse $\\nu$ (kinematic viscosity) with $v$.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"blue\">\n",
    "    \n",
    "$$\\frac{du}{dt} = \\underbrace{-\\frac{u_eu_e - u_wu_w}{\\Delta x} - \\frac{u_nv_n - u_sv_s}{\\Delta y}\n",
    "- \\frac{\\hat{\\tau}_{xx,e}-\\hat{\\tau}_{xx,w}}{\\Delta x} \n",
    "- \\frac{\\hat{\\tau}_{xy,n}-\\hat{\\tau}_{xy,s}}{\\Delta y}}_{H^u}\n",
    "- \\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x},$$\n",
    "\n",
    "$$\\frac{dv}{dt} = \\underbrace{-\\frac{v_nv_n - v_sv_s}{\\Delta y} - \\frac{v_eu_e - v_wu_w}{\\Delta x}\n",
    "- \\frac{\\hat{\\tau}_{yy,n}-\\hat{\\tau}_{yy,s}}{\\Delta y} \n",
    "- \\frac{\\hat{\\tau}_{yx,e}-\\hat{\\tau}_{yx,w}}{\\Delta x}}_{H^v}\n",
    "- \\frac{\\hat{P}_n-\\hat{P}_s}{\\Delta y}.$$\n",
    "\n",
    "    \n",
    "\\begin{align}\n",
    "    &\\mbox{cell center values: }&u_P = \\frac{u_e+u_w}{2}\\mbox{, etc.}, \\\\\n",
    "    &&v_P = \\frac{v_n+v_s}{2}\\mbox{, etc.}, \\\\\n",
    "\\end{align}\n",
    "    \n",
    "\\begin{align}\n",
    "    &\\mbox{auxiliary face values: }&u_n = \\frac{u_P+u_N}{2}, \\\\\n",
    "    &&u_s = \\frac{u_P+u_S}{2}, \\\\\n",
    "    &&v_e = \\frac{v_P+v_E}{2}, \\\\\n",
    "    &&v_w = \\frac{v_P+v_W}{2}, \\\\\n",
    "\\end{align}\n",
    "    \n",
    "\\begin{align}\n",
    "&\\hat{\\tau}_{xx,e} = -2\\nu\\left(\\frac{\\partial u}{\\partial x}\\right)_e = -2\\nu\\left(\\frac{u_E-u_P}{\\Delta x}\\right),\\,\\hat{\\tau}_{xx,w}\\mbox{ similarly}, \\\\\n",
    "&\\hat{\\tau}_{yy,n} = -2\\nu\\left(\\frac{\\partial v}{\\partial y}\\right)_n = -2\\nu\\left(\\frac{v_N-v_P}{\\Delta y}\\right),\\,\\hat{\\tau}_{yy,s}\\mbox{ similarly},\n",
    "\\end{align}\n",
    "    \n",
    "\\begin{align}\n",
    "&\\hat{\\tau}_{xy,n} = -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_n \n",
    "                     -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_n =  \n",
    "-\\nu\\left(\\frac{u_N-u_P}{\\Delta y}\\right)\n",
    "-\\nu\\left(\\frac{v_{n,i+1,j}-v_{n,i-1,j}}{2\\Delta x}\\right),\\,\\hat{\\tau}_{xy,s}\\mbox{ similarly}, \\\\\n",
    "&\\hat{\\tau}_{yx,e} = -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_e \n",
    "                     -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_e =  \n",
    "-\\nu\\left(\\frac{v_E-v_P}{\\Delta x}\\right)\n",
    "-\\nu\\left(\\frac{u_{e,i,j+1}-u_{e,i,j-1}}{2\\Delta y}\\right),\\,\\hat{\\tau}_{yx,w}\\mbox{ similarly}, \\\\\n",
    "\\end{align}\n",
    "    \n",
    "\\begin{align}\n",
    "&\\hat{P}_e = \\frac{1}{2}(\\hat{P}_{i,j}+\\hat{P}_{i+1,j}), & \\hat{P}_n = \\frac{1}{2}(\\hat{P}_{i,j+1}+\\hat{P}_{i,j}),\\\\\n",
    "&\\hat{P}_w = \\frac{1}{2}(\\hat{P}_{i,j}+\\hat{P}_{i-1,j}), & \\hat{P}_s = \\frac{1}{2}(\\hat{P}_{i,j}+\\hat{P}_{i,j-1}).\\\\\n",
    "\\end{align}\n",
    "    \n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuity\n",
    "* Continuity is applied to the face velocities.\n",
    "* The mass balance is given by\n",
    "$$\\int_A\\vec{v}\\cdot\\vec{n}dA = 0.$$\n",
    "* Integrate this over the P-cell to give\n",
    "$$(u_e-u_w)\\Delta y + (v_n-v_s)\\Delta x = 0.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discrete pressure equation\n",
    "* Apply a simple Euler step of size $\\Delta t=h$ to the $u$ and $v$ momentum equations velocities appearing in the above continuity equation. Here, $H$ is evaluated at time $n$, and $P$ at time $n+1$. This allows for an efficient explicit treatment of terms, with pressure at $n+1$ computed as shown below such that advancement of the velocity to the new time using the new pressure will result in a divergence-free velocity field.\n",
    "\n",
    "\\begin{align}\n",
    "u_e^{n+1} &= u_e^n + hH_e^{n,u} - \\frac{h}{\\Delta x}(\\hat{P}_E-\\hat{P}_P)^{n+1}, & H_e^u = \\frac{1}{2}(H_P^u + H_E^u) \\\\\n",
    "u_w^{n+1} &= u_w^n + hH_w^{n,u} - \\frac{h}{\\Delta x}(\\hat{P}_P-\\hat{P}_W)^{n+1}, & H_w^u = \\frac{1}{2}(H_P^u + H_W^u) \\\\\n",
    "v_n^{n+1} &= v_n^n + hH_n^{n,v} - \\frac{h}{\\Delta y}(\\hat{P}_N-\\hat{P}_P)^{n+1}, & H_n^v = \\frac{1}{2}(H_P^v + H_N^v) \\\\\n",
    "v_s^{n+1} &= v_s^n + hH_s^{n,v} - \\frac{h}{\\Delta y}(\\hat{P}_P-\\hat{P}_S)^{n+1}, & H_s^v = \\frac{1}{2}(H_P^v + H_S^v).\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We insert these into the continuity equation, and put terms involving $\\hat{P}$ on the left hand side, and all other terms on the right hand side. \n",
    "$$ \\hat{P}_S\\underbrace{\\left[\\frac{\\Delta t\\Delta x}{\\Delta y}\\right]}_{v_s} + \n",
    "\\hat{P}_W\\underbrace{\\left[\\frac{\\Delta t\\Delta y}{\\Delta x}\\right]}_{u_w} + \n",
    "\\hat{P}_P\\underbrace{  \\left[-\\frac{2\\Delta t\\Delta y}{\\Delta x} - \\frac{2\\Delta t\\Delta x}{\\Delta y}\\right]}_{(u_e,u_w) + (v_n,v_s)} + \n",
    "\\hat{P}_E\\underbrace{\\left[\\frac{\\Delta t\\Delta y}{\\Delta x}\\right]}_{u_e} + \n",
    "\\hat{P}_N\\underbrace{\\left[\\frac{\\Delta t\\Delta x}{\\Delta y}\\right]}_{v_n}  = $$\n",
    "$$-\\underbrace{\\Delta x(v_s^n+\\Delta tH_s^v)}_{v_s}\n",
    "-\\underbrace{\\Delta y(u_w^n+\\Delta tH_w^u)}_{u_w}\n",
    "+\\underbrace{\\Delta y(u_e^n+\\Delta tH_e^u)}_{u_e}\n",
    "+\\underbrace{\\Delta x(v_n^n+\\Delta tH_n^v)}_{v_n}\n",
    "$$\n",
    "* The terms are labeled to show which velocity components they arise from. This is convenient for dealing with boundary cells.\n",
    "* This is a linear system of equations in the form $A\\hat{P}=b$, where $A$ is a matrix and $\\hat{P}$ and $b$ are vectors.\n",
    "    * $A$ is the matrix of coefficients of $\\hat{P}$.\n",
    "        * There is a row in $A$ for each *i,j* cell in the grid.\n",
    "        * More on this below...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In the Cartesian grid shown, there are nine types of cells:\n",
    "    * **Corner** cells for the top-right, top-left, bottom-right, and bottom-left.\n",
    "    * **Edge** cells for the top, bottom, left, and right.\n",
    "    * **Interior** cells.\n",
    "    \n",
    "<img src=\"fig_4.png\" width=\"250\"/>\n",
    "\n",
    "* We have a P-equation for each type of cell. \n",
    "    * The equations differ in which velocity making up the equation is on a boundary face. \n",
    "    * For a cell with a velocity on a face, like and ET cell, the coefficient of $\\hat{P}_{i,j+1}$ is zero, the second term of the coefficient of $\\hat{P}_{i,j}$ is decreased (factor 2 $\\rightarrow 1$) and the corresponding term on the right hand side of the P-equation is replaced with the known face velocity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**$\\hat{P}$ coefficients for each cell type.**\n",
    "\n",
    "|Type| $P_{i,j-1}$ | $P_{i-1,j}$ | $P_{i,j}$ | $P_{i+1,j}$ | $P_{i,j+1}$ |\n",
    "|-------|-------|-------|-------|-------|-------|\n",
    "|I   | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $-\\frac{2\\Delta t\\Delta y}{\\Delta x}-\\frac{2\\Delta t\\Delta x}{\\Delta y}$ | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ |\n",
    "|CBL | $0$                                 | $0$                                 | $-\\frac{\\Delta t\\Delta y}{\\Delta x}-\\frac{ \\Delta t\\Delta x}{\\Delta y}$   | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ |\n",
    "|EB  | $0$                                 | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $-\\frac{2\\Delta t\\Delta y}{\\Delta x}-\\frac{\\Delta t\\Delta x}{\\Delta y}$  | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ |\n",
    "|CBR | $0$                                 | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $-\\frac{\\Delta t\\Delta y}{\\Delta x}-\\frac{ \\Delta t\\Delta x}{\\Delta y}$   | $0$                          | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ |\n",
    "|EL  | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ | $0$                                 | $-\\frac{\\Delta t\\Delta y}{\\Delta x}-\\frac{2\\Delta t\\Delta x}{\\Delta y}$  | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ |\n",
    "|ER  | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $-\\frac{\\Delta t\\Delta y}{\\Delta x}-\\frac{2\\Delta t\\Delta x}{\\Delta y}$  | $0$                          | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ |\n",
    "|CTL | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ | $0$                                 | $-\\frac{\\Delta t\\Delta y}{\\Delta x}-\\frac{ \\Delta t\\Delta x}{\\Delta y}$   | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $0$ |\n",
    "|ET  | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $-\\frac{2\\Delta t\\Delta y}{\\Delta x}-\\frac{\\Delta t\\Delta x}{\\Delta y}$  | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $0$ |\n",
    "|CTR | $\\frac{\\Delta t\\Delta x}{\\Delta y}$ | $\\frac{\\Delta t\\Delta y}{\\Delta x}$ | $-\\frac{\\Delta t\\Delta y}{\\Delta x}-\\frac{ \\Delta t\\Delta x}{\\Delta y}$   | $0$                          | $0$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**$b$ vector for each cell type.**\n",
    "\n",
    "| Type | $b_{v_s}$                       | $+b_{u_w}$                       | $+b_{u_e}$                    | $+b_{v_n}$                      |\n",
    "|------|---------------------------------|----------------------------------|-------------------------------|---------------------------------|\n",
    "| I    | $-\\Delta x(v_s^n+\\Delta tH_s^v)$| $-\\Delta y(u_w^n+\\Delta tH_w^u)$ | $\\Delta y(u_e+\\Delta tH_e^u)$ | $\\Delta x(v_n^n+\\Delta tH_n^v)$ |\n",
    "| CBL  | $-\\Delta x v_s$                 | $-\\Delta y u_w$                  | $\\Delta y(u_e+\\Delta tH_e^u)$ | $\\Delta x(v_n^n+\\Delta tH_n^v)$ |\n",
    "| EB   | $-\\Delta x v_s$                 | $-\\Delta y(u_w^n+\\Delta tH_w^u)$ | $\\Delta y(u_e+\\Delta tH_e^u)$ | $\\Delta x(v_n^n+\\Delta tH_n^v)$ |\n",
    "| CBR  | $-\\Delta x v_s$                 | $-\\Delta y(u_w^n+\\Delta tH_w^u)$ | $\\Delta y u_e$                | $\\Delta x(v_n^n+\\Delta tH_n^v)$ |\n",
    "| EL   | $-\\Delta x(v_s^n+\\Delta tH_s^v)$| $-\\Delta y u_w$                  | $\\Delta y(u_e+\\Delta tH_e^u)$ | $\\Delta x(v_n^n+\\Delta tH_n^v)$ |\n",
    "| ER   | $-\\Delta x(v_s^n+\\Delta tH_s^v)$| $-\\Delta y(u_w^n+\\Delta tH_w^u)$ | $\\Delta y u_e$                | $\\Delta x(v_n^n+\\Delta tH_n^v)$ |\n",
    "| CTL  | $-\\Delta x(v_s^n+\\Delta tH_s^v)$| $-\\Delta y u_w$                  | $\\Delta y(u_e+\\Delta tH_e^u)$ | $\\Delta x v_n$                  |\n",
    "| ET   | $-\\Delta x(v_s^n+\\Delta tH_s^v)$| $-\\Delta y(u_w^n+\\Delta tH_w^u)$ | $\\Delta y(u_e+\\Delta tH_e^u)$ | $\\Delta x v_n$                  |\n",
    "| CTR  | $-\\Delta x(v_s^n+\\Delta tH_s^v)$| $-\\Delta y(u_w^n+\\Delta tH_w^u)$ | $\\Delta y u_e$                | $\\Delta x v_n$                  |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation\n",
    "### 2-D lid-driven cavity\n",
    "* This is a box (cavity) with sliding walls. \n",
    "* Boundary conditions are simply zero normal flow through the domain boundaries, and constant tangential wall velocities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boilerplate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse import diags\n",
    "from scipy.sparse.linalg import spsolve\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "IJ = np.ix_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User specifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_user_specifications():\n",
    "    global nx, ny, Lx, Ly, ν, ubc_t, ubc_b, vbc_r, vbc_l, n_τ_run, cfl\n",
    "    \n",
    "    nx = 20        # number of grid points in x direction (P-grid)\n",
    "    ny = 30        # number of grid points in the y direction (P-grid)\n",
    "    Lx = 1.0          # domain length in x\n",
    "    Ly = 1.5          # domain length in y\n",
    "    ν  = 1.0        # kinematic viscosity\n",
    "    \n",
    "    ubc_t = 10.0      # u on top boundary\n",
    "    ubc_b = 0.0       # u on bottom boundary\n",
    "    vbc_r = 10.0      # v on right boundary\n",
    "    vbc_l = 0.0       # v on left boundary\n",
    "    \n",
    "    n_τ_run = 2        # number of box timescales to run for\n",
    "    cfl  = 0.05       # timestep size factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid and time parameters\n",
    "\n",
    "**Indexing:** \n",
    "* We use an x-y Cartesian grid. \n",
    "* For an array ```u(nx,ny)```, ```u[i,j]``` refers to x-index ```i``` and y-index ```j```.\n",
    "* As a matrix, we normally think of ```u[i,j]``` as row ```i``` and column ```j``` and visualize ```i``` varying vertically (rows change top-to-bottom) and ```j``` varying horizontally (columns change left-to-right).\n",
    "* However, in visualizing our grid, x is horizontal (increasing left-to-right), and y is vertical (increasing bottom to top).\n",
    "* This difference in the arrays versus the visual grid is only important in the tiling of the 2-D grid to a 1-D array for the pressure solve (see below), and in plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_grid_time_vars():\n",
    "    global x, y, Δx, Δy, tend, Δt, nsteps, u, v, uf, vf, P\n",
    "    \n",
    "    x  = np.linspace(Lx/nx/2,Lx-Lx/nx/2,nx)     # x-grid\n",
    "    y  = np.linspace(Ly/ny/2,Ly-Ly/ny/2,ny)     # x-grid\n",
    "    Δx = x[1] - x[0]              # x-grid spacing\n",
    "    Δy = y[1] - y[0]              # y-grid spacing\n",
    "    \n",
    "    τ_box  = (Lx+Ly)/2 / np.max(np.abs([ubc_t, ubc_b, vbc_r, vbc_l]))      # box timescale\n",
    "    tend   = τ_box * n_τ_run                                               # simulation run time\n",
    "    Δt     = cfl*np.min([Δx,Δy])/np.max(np.abs([ubc_t,ubc_b,vbc_r,vbc_l])) # timestep size\n",
    "    nsteps = int(tend/Δt)                                                  # number of timesteps\n",
    "    Δt     = tend/nsteps                                                   # timestep size\n",
    "    \n",
    "    #-------------------- set solution variables\n",
    "\n",
    "    P  = np.zeros((nx,ny))                # P = P/ρ (P_hat)\n",
    "    u  = np.zeros((nx,ny))                # cell center: u \n",
    "    v  = np.zeros((nx,ny))                # cell center: v \n",
    "    uf = np.zeros((nx+1,ny))              # face velocity (tilde), advecting\n",
    "    vf = np.zeros((nx,ny+1))              # face velocity (tilde), advecting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute H\n",
    "* Array slices are used to avoid loops for speed and simplicity\n",
    "* Note the use of the IJ = np.ix_ for certain array slices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_H():\n",
    "    \"\"\"\n",
    "    \"\"\"\n",
    "    global u, v\n",
    "    \n",
    "    uns   = np.zeros((nx,ny+1))\n",
    "    vew   = np.zeros((nx+1,ny))\n",
    "    τxxew = np.zeros((nx+1,ny))\n",
    "    τxyns = np.zeros((nx,ny+1))\n",
    "    τyyns = np.zeros((nx,ny+1))\n",
    "    τyxew = np.zeros((nx+1,ny))\n",
    "    Hu    = np.zeros((nx,ny))\n",
    "    Hv    = np.zeros((nx,ny))\n",
    "    Huf   = np.zeros((nx+1,ny))\n",
    "    Hvf   = np.zeros((nx,ny+1))\n",
    "    \n",
    "    #-----\n",
    "    \n",
    "    i = np.arange(1,nx)              # interior faces; edges are zero (by init)\n",
    "    j = np.arange(1,ny)              # interior faces; edges are zero (by init)\n",
    "    \n",
    "    u = (uf[1:,:] + uf[0:-1,:])/2\n",
    "    v = (vf[:,1:] + vf[:,0:-1])/2\n",
    "    \n",
    "    uns[:,0]  = ubc_b\n",
    "    uns[:,j]  = (u[:,j] + u[:,j-1])/2\n",
    "    uns[:,-1] = ubc_t\n",
    "    \n",
    "    vew[0,:]  = vbc_l\n",
    "    vew[i,:]  = (v[i,:] + v[i-1,:])/2\n",
    "    vew[-1,:] = vbc_r\n",
    "    \n",
    "    #----- τxxew, τyyns\n",
    "    \n",
    "    τxxew[i,:]  = -2*ν/Δx*(u[i,:] - u[i-1,:])\n",
    "    τyyns[:,j]  = -2*ν/Δy*(v[:,j] - v[:,j-1])\n",
    "    \n",
    "    #----- τxyns: shear viscous stress; two parts: du/dy, dv/dx; dv/dx=0 on t,b walls\n",
    "    j = np.arange(1,ny)\n",
    "    # du/dy part\n",
    "    τxyns[:,0]  = -ν/(Δy/2)*(u[:,0]-ubc_b)    # bottom row (xrows)\n",
    "    τxyns[:,j]  = -ν/Δy*(u[:,j]-u[:,j-1])     # interior rows (xrows)\n",
    "    τxyns[:,ny] = -ν/(Δy/2)*(ubc_t-u[:,ny-1]) # top row (xrows)\n",
    "    \n",
    "    # dv/dx part\n",
    "    i=0 \n",
    "    τxyns[i,j] -= ν/Δx*((vf[i,j]+vf[i+1,j])/2 - vbc_l)\n",
    "    i=np.arange(1,nx-1)\n",
    "    τxyns[IJ(i,j)] -= ν/(2*Δx)*(vf[IJ(i+1,j)]-vf[IJ(i-1,j)])\n",
    "    i=nx-1\n",
    "    τxyns[i,j] -= ν/Δx*(vbc_r - (vf[i,j]+vf[i-1,j])/2)\n",
    "    \n",
    "    #τxyns[:,0] = 0.0 #doldb\n",
    "    \n",
    "    #----- τyxew: shear viscous stress; two parts: dv/dx, du/dy; du/dy=0 on r,l walls\n",
    "    i = np.arange(1,nx)\n",
    "    # dv/dx part\n",
    "    τyxew[0,:]  = -ν/(Δx/2)*(v[0,:]-vbc_l)    # left col (ycols)\n",
    "    τyxew[i,:]  = -ν/Δx*(v[i,:]-v[i-1,:])     # interior cols (ycols)\n",
    "    τyxew[nx,:] = -ν/(Δx/2)*(vbc_r-v[nx-1,:]) # right col (ycols)\n",
    "    \n",
    "    # du/dy part\n",
    "    j=0 \n",
    "    τyxew[i,j] -= ν/Δy*((uf[i,j]+uf[i,j+1])/2 - ubc_b)\n",
    "    j=np.arange(1,ny-1)\n",
    "    τyxew[IJ(i,j)] -= ν/(2*Δy)*(uf[IJ(i,j+1)]-uf[IJ(i,j-1)])\n",
    "    j=ny-1\n",
    "    τyxew[i,j] -= ν/Δy*(ubc_t - (uf[i,j]+uf[i,j-1])/2)\n",
    "    \n",
    "    #τyxew[0,:]  = 0.0 # doldb\n",
    "    #τyxew[-1,:] = 0.0 # doldb\n",
    "    \n",
    "    #----- Hu, Hv\n",
    "    \n",
    "    ie = np.arange(1,nx+1)\n",
    "    iw = ie-1\n",
    "    jn = np.arange(1,ny+1)\n",
    "    js = jn-1\n",
    "    \n",
    "    Hu = -(uf[ie,:]*uf[ie,:]  - uf[iw,:]*uf[iw,:] )/Δx \\\n",
    "         -(vf[:,jn]*uns[:,jn] - vf[:,js]*uns[:,js])/Δy \\\n",
    "         -(τxxew[ie,:]        - τxxew[iw,:])       /Δx \\\n",
    "         -(τxyns[:,jn]        - τxyns[:,js])       /Δy\n",
    "    \n",
    "    Hv = -(vf[:,jn]*vf[:,jn]  - vf[:,js]*vf[:,js] )/Δy \\\n",
    "         -(uf[ie,:]*vew[ie,:] - uf[iw,:]*vew[iw,:])/Δx \\\n",
    "         -(τyyns[:,jn]        - τyyns[:,js])       /Δy \\\n",
    "         -(τyxew[ie,:]        - τyxew[iw,:])       /Δx\n",
    "    \n",
    "    #----- Huf, Hvf (boundary values are zero by initialization)\n",
    "    \n",
    "    i = np.arange(1,nx)\n",
    "    j = np.arange(1,ny)\n",
    "    \n",
    "    Huf[i,:] = 0.5*(Hu[i,:]+Hu[i-1,:])\n",
    "    Hvf[:,j] = 0.5*(Hv[:,j]+Hv[:,j-1])\n",
    "    \n",
    "    #-----\n",
    "    \n",
    "    return Huf, Hvf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pressure solver\n",
    "\n",
    "* Solving $\\hat{P}$ on the P-grid:\n",
    "\n",
    "<img src=\"Pgrid2.png\" width=\"200\"/>\n",
    "\n",
    "* We have an algebraic equation for each cell. The collection of equations will be an $A\\hat{P}=b$ system.\n",
    "* We need to create $A$ and $b$, where $A$ is the matrix of coefficients of $\\hat{P}$ and $b$ is a vector.\n",
    "* The equations were shown previously.\n",
    "    * We had 9 equation types: (1) interior, (4) sides, and (4) corners.\n",
    "    * The code below parallels the tables shown with the equations.\n",
    "* Each cell has a $\\hat{P}$ coefficient for itself and four neighbors. \n",
    "    * This gives an ```(nx,ny)``` array for the base point and each neighbor.\n",
    "* Similarly, we have an ```(nx,ny)``` array for $b$.\n",
    "* Once the $\\hat{P}$ coefficient and $b$ arrays are set, we need to reorder them from 2-D arrays to a 1-D array.\n",
    "    * We tile the points as indicated in the grid above: that is, counting cells in the x-direction from low to high y.\n",
    "    * This is done with numpy's reshape function.\n",
    "        * Since we are numbering along x, and x varies along columns in our arrays, we are using a column-major ordering, as in Fortran (as opposed to row-major ordering as in C and here in Python). Specify ```order='F'``` in the reshape functions.\n",
    "* For incompressible flows, pressure is only determined up to an additive constant: only pressure differences matter. This is evident in pressure only appearing in a gradient; adding any constant to $\\hat{P}$ doesn't change the term.\n",
    "    * To keep pressure from drifting, at the end of the pressure solve, we subtract the mean pressure so that the average is always pinned at zero.\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_P(h):\n",
    "    \"\"\"\n",
    "    Set up and solve the AP=b system, where A is a matrix, P (=Phat) and b are vectors.\n",
    "    \"\"\"\n",
    "    \n",
    "    nP = nx*ny     # total grid points solved (all P-grid cells)\n",
    "    \n",
    "    b    = np.zeros((nx,ny))\n",
    "    cP   = np.zeros((nx,ny))\n",
    "    cE   = np.full((nx,ny),h*Δy/Δx)\n",
    "    cW   = np.full((nx,ny),h*Δy/Δx)\n",
    "    cN   = np.full((nx,ny),h*Δx/Δy)\n",
    "    cS   = np.full((nx,ny),h*Δx/Δy)\n",
    "    \n",
    "    #-------------------- \n",
    "    \n",
    "    # Interior\n",
    "    i = np.arange(1,nx-1); j = np.arange(1,ny-1)\n",
    "    b[IJ(i,j)]    = -Δx*(vf[IJ(i,j)]+h*Hvf[IJ(i,j)]) - Δy*(uf[IJ(i,j)]+h*Huf[IJ(i,j)]) + Δy*(uf[IJ(i+1,j)]+h*Huf[IJ(i+1,j)]) + Δx*(vf[IJ(i,j+1)]+h*Hvf[IJ(i,j+1)])\n",
    "    cP[IJ(i,j)]   = -2*h*Δy/Δx - 2*h*Δx/Δy\n",
    "    \n",
    "    # Corner bottom left\n",
    "    i = 0; j = 0\n",
    "    b[i,j]        = -Δx*vf[i,j]                      - Δy*uf[i,j]                      + Δy*(uf[i+1,j]+h*Huf[i+1,j])         + Δx*(vf[i,j+1]+h*Hvf[i,j+1])\n",
    "    cP[i,j]       = -h*Δy/Δx - h*Δx/Δy\n",
    "    cW[i,j]       =  0.0\n",
    "    cS[i,j]       =  0.0\n",
    "    \n",
    "    # Side bottom\n",
    "    i = np.arange(1,nx-1); j = 0\n",
    "    b[i,j]        = -Δx*vf[i,j]                      - Δy*(uf[i,j]+h*Huf[i,j])         + Δy*(uf[i+1,j]+h*Huf[i+1,j])         + Δx*(vf[i,j+1]+h*Hvf[i,j+1])\n",
    "    cP[i,j]       = -2*h*Δy/Δx - h*Δx/Δy\n",
    "    cS[i,j]       =  0.0\n",
    "    \n",
    "    # Corner bottom right\n",
    "    i = nx-1; j = 0\n",
    "    b[i,j]        = -Δx*vf[i,j]                      - Δy*(uf[i,j]+h*Huf[i,j])         + Δy*uf[i+1,j]                        + Δx*(vf[i,j+1]+h*Hvf[i,j+1])\n",
    "    cP[i,j]       = -h*Δy/Δx - h*Δx/Δy\n",
    "    cE[i,j]       =  0.0\n",
    "    cS[i,j]       =  0.0\n",
    "    \n",
    "    # Side left\n",
    "    i = 0; j = np.arange(1,ny-1)\n",
    "    b[i,j]        = -Δx*(vf[i,j]+h*Hvf[i,j])         - Δy*uf[i,j]                      + Δy*(uf[i+1,j]+h*Huf[i+1,j])         + Δx*(vf[i,j+1]+h*Hvf[i,j+1])\n",
    "    cP[i,j]       = -h*Δy/Δx - 2*h*Δx/Δy\n",
    "    cW[i,j]       =  0.0\n",
    "    \n",
    "    # Side right\n",
    "    i = nx-1; j = np.arange(1,ny-1)\n",
    "    b[i,j]        = -Δx*(vf[i,j]+h*Hvf[i,j])         - Δy*(uf[i,j]+h*Huf[i,j])         + Δy*uf[i+1,j]                        + Δx*(vf[i,j+1]+h*Hvf[i,j+1])\n",
    "    cP[i,j]       = -h*Δy/Δx - 2*h*Δx/Δy\n",
    "    cE[i,j]       =  0.0\n",
    "    \n",
    "    # Corner top left\n",
    "    i = 0; j = ny-1\n",
    "    b[i,j]        = -Δx*(vf[i,j]+h*Hvf[i,j])         - Δy*uf[i,j]                      + Δy*(uf[i+1,j]+h*Huf[i+1,j])         + Δx*vf[i,j+1]\n",
    "    cP[i,j]       = -h*Δy/Δx - h*Δx/Δy\n",
    "    cW[i,j]       =  0.0\n",
    "    cN[i,j]       =  0.0\n",
    "    \n",
    "    # Side top\n",
    "    i = np.arange(1,nx-1); j = ny-1\n",
    "    b[i,j]        = -Δx*(vf[i,j]+h*Hvf[i,j])         - Δy*(uf[i,j]+h*Huf[i,j])         + Δy*(uf[i+1,j]+h*Huf[i+1,j])         + Δx*vf[i,j+1]\n",
    "    cP[i,j]       = -2*h*Δy/Δx - h*Δx/Δy\n",
    "    cN[i,j]       =  0.0\n",
    "    \n",
    "    # Corner top right\n",
    "    i = nx-1; j = ny-1\n",
    "    b[i,j]        = -Δx*(vf[i,j]+h*Hvf[i,j])         - Δy*(uf[i,j]+h*Huf[i,j])         + Δy*uf[i+1,j]                       + Δx*vf[i,j+1]\n",
    "    cP[i,j]       = -h*Δy/Δx - h*Δx/Δy\n",
    "    cE[i,j]       =  0.0\n",
    "    cN[i,j]       =  0.0\n",
    "    \n",
    "    #---------------------------------\n",
    "    \n",
    "    b    = np.reshape(b,    nP, order='F')\n",
    "    \n",
    "    cP   = np.reshape(cP,   nP, order='F')\n",
    "    cE   = np.reshape(cE,   nP, order='F')\n",
    "    cW   = np.reshape(cW,   nP, order='F')\n",
    "    cS   = np.reshape(cS,   nP, order='F')\n",
    "    cN   = np.reshape(cN,   nP, order='F')\n",
    "    \n",
    "    A = diags([cS[nx:], cW[1:], cP, cE[:-1], cN[:-nx]], [-nx, -1, 0, 1, nx], format='csr')\n",
    "    \n",
    "    #---------------------------------\n",
    "    \n",
    "    P = spsolve(A,b)\n",
    "    \n",
    "    P -= np.average(P)\n",
    "    \n",
    "    P = np.reshape(P, (nx,ny),order='F')\n",
    "    \n",
    "    return P\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Driver and integrator\n",
    "\n",
    "* Solve the problem with a simple Explicit Euler integration.\n",
    "    * Higher order integrators are possible, with minor adjustments to the pressure solve.\n",
    "* To monitor the approach to steady state, record the average kinetic energy on the domain at each timestep.\n",
    "* The velocity magnitude is stored for convenient plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.39 s, sys: 86.6 ms, total: 2.48 s\n",
      "Wall time: 2.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "set_user_specifications()\n",
    "set_grid_time_vars()\n",
    "\n",
    "ke = np.zeros(nsteps+1)\n",
    "times = np.linspace(0,tend,nsteps+1)\n",
    "\n",
    "for k in range(nsteps):\n",
    "    \n",
    "    #-----\n",
    "    \n",
    "    Huf,Hvf = get_H()\n",
    "    \n",
    "    #-----\n",
    "    \n",
    "    P = solve_P(Δt)\n",
    "    \n",
    "    #-----\n",
    "    \n",
    "    i = np.arange(1,nx) \n",
    "    uf[i,:] = uf[i,:] + Δt*Huf[i,:] - Δt*(P[i,:]-P[i-1,:])/Δx\n",
    "    \n",
    "    j = np.arange(1,ny) \n",
    "    vf[:,j] = vf[:,j] + Δt*Hvf[:,j] - Δt*(P[:,j]-P[:,j-1])/Δy\n",
    "    \n",
    "    #-----\n",
    "    \n",
    "    velmag = np.sqrt(u*u + v*v)\n",
    "    ke[k+1] = 0.5*(np.average(velmag**2))\n",
    "    \n",
    "    #i = np.arange(0,nx)\n",
    "    #j = np.arange(0,ny)\n",
    "    #mb = ( (uf[i+1,:] - uf[i,:])*Δy + (vf[:,j+1] - vf[:,j])*Δx ) / velmag\n",
    "    #print(np.linalg.norm(mb))\n",
    "    \n",
    "    \n",
    "    \n",
    "velmag = np.sqrt(u*u + v*v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X,Y = np.meshgrid(x,y)\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,10))\n",
    "\n",
    "ax1.set_aspect('equal', adjustable='box')\n",
    "ax1.streamplot(x,y,u.T,v.T, density=2.5, linewidth=1, arrowsize=1.001, color=velmag.T)\n",
    "ax1.set_title(r'$|\\vec{v}|$')\n",
    "ax1.set_xlim([0,Lx])\n",
    "ax1.set_ylim([0,Ly])\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([]);\n",
    "\n",
    "ax2.set_aspect('equal', adjustable='box')\n",
    "ax2.contourf(X,Y,P.T,40)\n",
    "ax2.set_title(r'$P/\\rho$')\n",
    "ax2.set_xlim([0,Lx])\n",
    "ax2.set_ylim([0,Ly])\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([]);\n",
    "\n",
    "plt.figure(figsize=(3.5,3))\n",
    "plt.plot(times,ke)\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('KE');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}