diff --git a/.gitignore b/.gitignore
index ef83a8b..c92fa1f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -138,3 +138,5 @@ White Giant - Expander Cycle.ipynb
 White Giant - Expander Cycle Iterative.ipynb
 White Giant - Spiral Channels 3.ipynb
 White Giant - Spiral Channels 4.ipynb
+White Giant - Vertical Channels 1.ipynb
+White Giant - Vertical Channels 1 (nitrous coolant).ipynb
diff --git a/Example - Ariane 5 Vulcain.ipynb b/Example - Ariane 5 Vulcain.ipynb
index 2d8f084..cc3beca 100644
--- a/Example - Ariane 5 Vulcain.ipynb	
+++ b/Example - Ariane 5 Vulcain.ipynb	
@@ -428,7 +428,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 780x520 with 2 Axes>"
       ]
@@ -442,7 +442,7 @@
    "source": [
     "#Compare results with data from paper\n",
     "fig, ax1 = plt.subplots()\n",
-    "fig.suptitle(\"Comparison of Vulcain Cooling Data (No fudge factor)\")\n",
+    "fig.suptitle(\"Comparison of Vulcain Cooling Data (No Fudge Factor)\")\n",
     "\n",
     "#Plot heat transfer rates\n",
     "ax1.plot(np.array(heat_transfer_rate[\"x\"]) - throat_pos, heat_transfer_rate[\"y\"], label=\"qdot (Original)\", color=\"red\")\n",
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -543,20 +543,6 @@
     "\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/_autosummary/bamboo.cooling.html b/docs/_autosummary/bamboo.cooling.html
index 4940222..7e835f7 100644
--- a/docs/_autosummary/bamboo.cooling.html
+++ b/docs/_autosummary/bamboo.cooling.html
@@ -193,19 +193,22 @@
 <tr class="row-even"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_coolant_dittus_boelter" title="bamboo.cooling.h_coolant_dittus_boelter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_coolant_dittus_boelter</span></code></a>(rho, V, D, mu, Pr, k)</p></td>
 <td><p>Dittus-Boelter equation for convective heat transfer coefficient.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_coolant_rpe" title="bamboo.cooling.h_coolant_rpe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_coolant_rpe</span></code></a>(A, D, mdot, mu, k, c_bar, rho)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_coolant_gnielinski" title="bamboo.cooling.h_coolant_gnielinski"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_coolant_gnielinski</span></code></a>(rho, V, D, mu, Pr, k, f)</p></td>
+<td><p>Convective heat transfer coefficient for the coolant side, using Gnielinski’s correlation.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_coolant_rpe" title="bamboo.cooling.h_coolant_rpe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_coolant_rpe</span></code></a>(A, D, mdot, mu, k, c_bar, rho)</p></td>
 <td><p>Get the convective heat transfer coefficient for the coolant side.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_coolant_sieder_tate" title="bamboo.cooling.h_coolant_sieder_tate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_coolant_sieder_tate</span></code></a>(rho, V, D, mu_bulk, …)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_coolant_sieder_tate" title="bamboo.cooling.h_coolant_sieder_tate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_coolant_sieder_tate</span></code></a>(rho, V, D, mu_bulk, …)</p></td>
 <td><p>Sieder-Tate equation for convective heat transfer coefficient.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_gas_bartz" title="bamboo.cooling.h_gas_bartz"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_gas_bartz</span></code></a>(D, cp_inf, mu_inf, Pr_inf, …)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_gas_bartz" title="bamboo.cooling.h_gas_bartz"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_gas_bartz</span></code></a>(D, cp_inf, mu_inf, Pr_inf, …)</p></td>
 <td><p>Bartz equation, using Equation (8-23) from page 312 of RPE 7th edition (Reference [2]).</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_gas_bartz_sigma" title="bamboo.cooling.h_gas_bartz_sigma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_gas_bartz_sigma</span></code></a>(c_star, At, A, pc, Tc, M, …)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_gas_bartz_sigma" title="bamboo.cooling.h_gas_bartz_sigma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_gas_bartz_sigma</span></code></a>(c_star, At, A, pc, Tc, M, …)</p></td>
 <td><p>Bartz heat transfer equation using the sigma correlation, from Reference [6].</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_gas_rpe" title="bamboo.cooling.h_gas_rpe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_gas_rpe</span></code></a>(D, M, T, rho, gamma, R, mu, k, Pr)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="../index.html#bamboo.cooling.h_gas_rpe" title="bamboo.cooling.h_gas_rpe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">h_gas_rpe</span></code></a>(D, M, T, rho, gamma, R, mu, k, Pr)</p></td>
 <td><p>Get the convective heat transfer coefficient on the gas side.</p></td>
 </tr>
 </tbody>
diff --git a/docs/_autosummary/bamboo.main.html b/docs/_autosummary/bamboo.main.html
index 2613a43..3562dce 100644
--- a/docs/_autosummary/bamboo.main.html
+++ b/docs/_autosummary/bamboo.main.html
@@ -164,17 +164,13 @@
 <li><p>1D flow.</p></li>
 <li><p>Isentropic flow.</p></li>
 <li><p>Perfect gases.</p></li>
+<li><p>No axial heat transfer (only radial) - this becomes inaccurate when there are large axial temperature gradients (e.g. sometimes along walls near the throat)</p></li>
 </ul>
 </dd>
 <dt>Conventions:</dt><dd><ul class="simple">
 <li><p>Position (x) along the nozzle is defined by: x = 0 at the throat, x &lt; 0 in the combustion chamber, x &gt; 0 in the diverging section of the nozzle.</p></li>
 </ul>
 </dd>
-<dt>Known issues:</dt><dd><ul class="simple">
-<li><p>A hardcoded fix is in place for using area ratios outside the Rao angle data range (it tricks the code into making something close to a 15 degree cone). A more robust fix would be better.</p></li>
-<li><p>h_gas_model = ‘2’ doesn’t seem to work very well (if at all) right now.</p></li>
-</ul>
-</dd>
 <dt>Room for improvement:</dt><dd><ul class="simple">
 <li><p>Should check if the Engine.channel_geometry() function is working as intended.</p></li>
 <li><p>Unsure if the first step (i = 0) in Engine.steady_heating_analysis() is dealt with correctly when using h_gas_model == ‘3’.</p></li>
diff --git a/docs/_sources/_autosummary/bamboo.cooling.rst.txt b/docs/_sources/_autosummary/bamboo.cooling.rst.txt
index 4a182b1..c6b96ef 100644
--- a/docs/_sources/_autosummary/bamboo.cooling.rst.txt
+++ b/docs/_sources/_autosummary/bamboo.cooling.rst.txt
@@ -15,6 +15,7 @@
    
       black_body
       h_coolant_dittus_boelter
+      h_coolant_gnielinski
       h_coolant_rpe
       h_coolant_sieder_tate
       h_gas_bartz
diff --git a/docs/build/doctrees/_autosummary/bamboo.cooling.doctree b/docs/build/doctrees/_autosummary/bamboo.cooling.doctree
index ae04194..0201d95 100644
Binary files a/docs/build/doctrees/_autosummary/bamboo.cooling.doctree and b/docs/build/doctrees/_autosummary/bamboo.cooling.doctree differ
diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle
index 0dc46c7..00fbcd4 100644
Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ
diff --git a/docs/build/doctrees/index.doctree b/docs/build/doctrees/index.doctree
index 7e791a0..94a9cf2 100644
Binary files a/docs/build/doctrees/index.doctree and b/docs/build/doctrees/index.doctree differ
diff --git a/docs/genindex.html b/docs/genindex.html
index f425d8b..b411196 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -275,17 +275,21 @@ <h2 id="C">C</h2>
 </li>
       <li><a href="index.html#bamboo.main.ChamberConditions">ChamberConditions (class in bamboo.main)</a>
 </li>
-      <li><a href="index.html#bamboo.main.Engine.check_separation">check_separation() (bamboo.main.Engine method)</a>
+      <li><a href="index.html#bamboo.cooling.CoolingJacket.channel_height">channel_height() (bamboo.cooling.CoolingJacket method)</a>
+</li>
+      <li><a href="index.html#bamboo.main.Engine.channel_path_lengths">channel_path_lengths() (bamboo.main.Engine method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#bamboo.cooling.CoolingJacket.channel_width">channel_width() (bamboo.cooling.CoolingJacket method)</a>
+</li>
+      <li><a href="index.html#bamboo.main.Engine.check_separation">check_separation() (bamboo.main.Engine method)</a>
+</li>
       <li><a href="index.html#bamboo.main.Engine.coolant_dynamic_pressure">coolant_dynamic_pressure() (bamboo.main.Engine method)</a>
 </li>
       <li><a href="index.html#bamboo.main.Engine.coolant_friction_factor">coolant_friction_factor() (bamboo.main.Engine method)</a>
 </li>
       <li><a href="index.html#bamboo.main.Engine.coolant_p0_drop">coolant_p0_drop() (bamboo.main.Engine method)</a>
-</li>
-      <li><a href="index.html#bamboo.main.Engine.coolant_path_length">coolant_path_length() (bamboo.main.Engine method)</a>
 </li>
       <li><a href="index.html#bamboo.cooling.CoolingJacket.coolant_velocity">coolant_velocity() (bamboo.cooling.CoolingJacket method)</a>
 </li>
@@ -353,12 +357,14 @@ <h2 id="H">H</h2>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="index.html#bamboo.cooling.h_coolant_dittus_boelter">h_coolant_dittus_boelter() (in module bamboo.cooling)</a>
 </li>
-      <li><a href="index.html#bamboo.cooling.h_coolant_rpe">h_coolant_rpe() (in module bamboo.cooling)</a>
+      <li><a href="index.html#bamboo.cooling.h_coolant_gnielinski">h_coolant_gnielinski() (in module bamboo.cooling)</a>
 </li>
-      <li><a href="index.html#bamboo.cooling.h_coolant_sieder_tate">h_coolant_sieder_tate() (in module bamboo.cooling)</a>
+      <li><a href="index.html#bamboo.cooling.h_coolant_rpe">h_coolant_rpe() (in module bamboo.cooling)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#bamboo.cooling.h_coolant_sieder_tate">h_coolant_sieder_tate() (in module bamboo.cooling)</a>
+</li>
       <li><a href="index.html#bamboo.cooling.h_gas_bartz">h_gas_bartz() (in module bamboo.cooling)</a>
 </li>
       <li><a href="index.html#bamboo.cooling.h_gas_bartz_sigma">h_gas_bartz_sigma() (in module bamboo.cooling)</a>
diff --git a/docs/index.html b/docs/index.html
index 73ac694..baff093 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -187,17 +187,13 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <li><p>1D flow.</p></li>
 <li><p>Isentropic flow.</p></li>
 <li><p>Perfect gases.</p></li>
+<li><p>No axial heat transfer (only radial) - this becomes inaccurate when there are large axial temperature gradients (e.g. sometimes along walls near the throat)</p></li>
 </ul>
 </dd>
 <dt>Conventions:</dt><dd><ul class="simple">
 <li><p>Position (x) along the nozzle is defined by: x = 0 at the throat, x &lt; 0 in the combustion chamber, x &gt; 0 in the diverging section of the nozzle.</p></li>
 </ul>
 </dd>
-<dt>Known issues:</dt><dd><ul class="simple">
-<li><p>A hardcoded fix is in place for using area ratios outside the Rao angle data range (it tricks the code into making something close to a 15 degree cone). A more robust fix would be better.</p></li>
-<li><p>h_gas_model = ‘2’ doesn’t seem to work very well (if at all) right now.</p></li>
-</ul>
-</dd>
 <dt>Room for improvement:</dt><dd><ul class="simple">
 <li><p>Should check if the Engine.channel_geometry() function is working as intended.</p></li>
 <li><p>Unsure if the first step (i = 0) in Engine.steady_heating_analysis() is dealt with correctly when using h_gas_model == ‘3’.</p></li>
@@ -1135,7 +1131,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 
 <dl class="py method">
 <dt id="bamboo.main.Engine.add_cooling_jacket">
-<code class="sig-name descname"><span class="pre">add_cooling_jacket</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inner_wall_material</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inlet_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inlet_p0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coolant_transport</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mdot_coolant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[-</span> <span class="pre">1000,</span> <span class="pre">1000]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">configuration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spiral'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.main.Engine.add_cooling_jacket" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">add_cooling_jacket</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inner_wall_material</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inlet_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inlet_p0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coolant_transport</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mdot_coolant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">configuration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spiral'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.main.Engine.add_cooling_jacket" title="Permalink to this definition">¶</a></dt>
 <dd><p>Container for cooling jacket information - e.g. for regenerative cooling.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1145,7 +1141,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <li><p><strong>inlet_p0</strong> (<em>float</em>) – Inlet coolant stagnation pressure (Pa)</p></li>
 <li><p><strong>coolant_transport</strong> (<a class="reference internal" href="#bamboo.cooling.TransportProperties" title="bamboo.cooling.TransportProperties"><em>TransportProperties</em></a>) – Container for the coolant transport properties.</p></li>
 <li><p><strong>mdot_coolant</strong> (<em>float</em>) – Coolant mass flow rate (kg/s)</p></li>
-<li><p><strong>xs</strong> (<em>list</em>) – x positions that the cooling jacket starts and ends at, [x_min, x_max]. Defaults to [-1000, 1000].</p></li>
+<li><p><strong>xs</strong> (<em>list</em>) – x positions that the cooling jacket starts and ends at, [x_min, x_max]. By default will encompass the entire engine.</p></li>
 <li><p><strong>configuration</strong> (<em>str</em><em>, </em><em>optional</em>) – Options include ‘spiral’ and ‘vertical’. Defaults to “vertical”.</p></li>
 <li><p><strong>has_ablative</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether or not the engine has an ablative.</p></li>
 </ul>
@@ -1154,7 +1150,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <dd class="field-even"><ul class="simple">
 <li><p><strong>blockage_ratio</strong> (<em>float</em>) – This is the proportion (by area) of the channel cross section occupied by ribs.</p></li>
 <li><p><strong>number_of_ribs</strong> (<em>int</em>) – Only relevant if ‘blockage_ratio’ !=0. This is the number of ribs present in the cooling channel. For spiral channels this is the number of ribs ‘per pitch’ - it is numerically equal to the number of parallel spiral channels.</p></li>
-<li><p><strong>channel_height</strong> (<em>float</em>) – This is the height of the channels, in the radial direction (m).</p></li>
+<li><p><strong>channel_height</strong> (<em>float</em><em> or </em><em>list</em>) – This is the height of the channels, in the radial direction (m). Can be a constant (float) or list. If list, it must be an channel heights at regularly spaced x positions, which will be stretched to fit the xs input.</p></li>
 <li><p><strong>channel_width</strong> (<em>float</em>) – Only relevant if configuration = ‘spiral’. This is the total width (i.e. pitch) of the cooling channels (m).</p></li>
 <li><p><strong>outer_wall_material</strong> (<a class="reference internal" href="#bamboo.cooling.Material" title="bamboo.cooling.Material"><em>Material</em></a>) – Wall material for the outer liner.</p></li>
 </ul>
@@ -1210,10 +1206,10 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 </dd></dl>
 
 <dl class="py method">
-<dt id="bamboo.main.Engine.coolant_path_length">
-<code class="sig-name descname"><span class="pre">coolant_path_length</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_of_sections</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.main.Engine.coolant_path_length" title="Permalink to this definition">¶</a></dt>
+<dt id="bamboo.main.Engine.channel_path_lengths">
+<code class="sig-name descname"><span class="pre">channel_path_lengths</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_of_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.main.Engine.channel_path_lengths" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
-<dt>Finds the path length of the coolant in the jacket from engine geometry and channel configuration.</dt><dd><p>Number_of_sections must be equal to number_of_points when used in a heating analysis.</p>
+<dt>Finds the path length travelled by the coolant in between each pair of x points.</dt><dd><p>Number_of_points input must be equal to number_of_points used in a heating analysis.</p>
 </dd>
 </dl>
 <div class="admonition note">
@@ -1222,10 +1218,10 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 </div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>number_of_sections</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of sections to split path into. Defaults to 1000.</p>
+<dd class="field-odd"><p><strong>number_of_points</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of discretised points to split path into. Defaults to 1000.</p>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p>Discretised coolant path length array with “number_of_sections” elements. (m).</p>
+<dd class="field-even"><p>Coolant path lengths (dl) between each pair of x points. e.g. discretised_length[10] = path length between xs[10] and xs[11]</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>array</p>
@@ -1333,14 +1329,14 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 
 <dl class="py method">
 <dt id="bamboo.main.Engine.steady_heating_analysis">
-<code class="sig-name descname"><span class="pre">steady_heating_analysis</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_of_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_gas_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bartz-sigma'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_coolant_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sieder-tate'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">to_json</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'heating_output.json'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.main.Engine.steady_heating_analysis" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname"><span class="pre">steady_heating_analysis</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_of_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_gas_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bartz-sigma'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_coolant_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gnielinski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">to_json</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'heating_output.json'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.main.Engine.steady_heating_analysis" title="Permalink to this definition">¶</a></dt>
 <dd><p>Steady state heating analysis. Can be used for regenarative cooling, or combined regenerative and ablative cooling.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>number_of_points</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of discrete points to divide the engine into. Defaults to 1000.</p></li>
 <li><p><strong>h_gas_model</strong> (<em>str</em><em>, </em><em>optional</em>) – Equation to use for the gas side convective heat transfer coefficients. Options are ‘rpe’, ‘bartz’ and ‘bartz-sigma’. Defaults to “bartz-sigma”.</p></li>
-<li><p><strong>h_coolant_model</strong> (<em>str</em><em>, </em><em>optional</em>) – Equation to use for the coolant side convective heat transfer coefficients. Options are ‘rpe’, ‘sieder-tate’ and ‘dittus-boelter’. Defaults to “sieder-tate”.</p></li>
+<li><p><strong>h_coolant_model</strong> (<em>str</em><em>, </em><em>optional</em>) – Equation to use for the coolant side convective heat transfer coefficients. Options are ‘rpe’, ‘sieder-tate’, ‘dittus-boelter’ and ‘gnielinski’. Defaults to “gnielinski”.</p></li>
 <li><p><strong>to_json</strong> (<em>str</em><em> or </em><em>bool</em><em>, </em><em>optional</em>) – Directory to export a .JSON file to, containing simulation results. If False, no .JSON file is saved. Defaults to ‘heating_output.json’.</p></li>
 </ul>
 </dd>
@@ -1350,7 +1346,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 </dl>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>See the bamboo.cooling module for details of each h_gas and h_coolant option. Defaults are Bartz (using sigma correlation) for gas side, and Sieder-Tate for coolant side. These are believed to be the most accurate.</p>
+<p>See the bamboo.cooling module for details of each h_gas and h_coolant option. Defaults are Bartz (using sigma correlation) for gas side, and Gnielinski for coolant side.</p>
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1368,6 +1364,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <li><p>”q_dot” : Heat transfer rate per unit length (axially along the engine) (W/m)</p></li>
 <li><p>”h_gas” : Convective heat transfer rate for the exhaust gas side</p></li>
 <li><p>”h_coolant” : Convective heat transfer rate for the coolant side</p></li>
+<li><p>”enthalpy_coolant” : Coolant specific enthalpy (J/kg) - will only contain data if enthalpy functions were provided.</p></li>
 <li><p>”boil_off_position” : x position of any coolant boil off. Equal to None if the coolant does not boil.</p></li>
 <li><p>(and some more)</p></li>
 </ul>
@@ -1410,6 +1407,10 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <dt>Perform stress analysis on the liner, using a cooling result.</dt><dd><p>Results should be taken only as a first approximation of some key stresses.</p>
 </dd>
 </dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Will likely not work right now, as it has not been updated to accodomate variable cooling channel heights</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1585,7 +1586,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>A</strong> (<em>float</em>) – Coolant flow area (m^2)</p></li>
-<li><p><strong>D</strong> (<em>float</em>) – Coolant channel effective diameter (m)</p></li>
+<li><p><strong>D</strong> (<em>float</em>) – Coolant channel hydraulic diameter (m)</p></li>
 <li><p><strong>mdot</strong> (<em>float</em>) – Coolant mass flow rate (kg/s)</p></li>
 <li><p><strong>mu</strong> (<em>float</em>) – Coolant absolute viscosity (Pa s)</p></li>
 <li><p><strong>k</strong> (<em>float</em>) – Coolant thermal conductivity (W/m/K)</p></li>
@@ -1611,7 +1612,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>rho</strong> (<em>float</em>) – Coolant bulk density (kg/m^3)</p></li>
 <li><p><strong>V</strong> (<em>float</em>) – Coolant bulk velocity (m/s)</p></li>
-<li><p><strong>D</strong> (<em>float</em>) – Effective diameter of pipe (m)</p></li>
+<li><p><strong>D</strong> (<em>float</em>) – Hydraulic diameter of pipe (m)</p></li>
 <li><p><strong>mu_bulk</strong> (<em>float</em>) – Absolute viscosity of the coolant at the bulk temperature (Pa s).</p></li>
 <li><p><strong>mu_wall</strong> (<em>float</em>) – Absolute viscosity of the coolant at the wall temperature (Pa s).</p></li>
 <li><p><strong>Pr</strong> (<em>float</em>) – Bulk Prandtl number of the coolant.</p></li>
@@ -1636,7 +1637,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>rho</strong> (<em>float</em>) – Coolant bulk density (kg/m^3).</p></li>
 <li><p><strong>V</strong> (<em>float</em>) – Coolant bulk velocity (m/s)</p></li>
-<li><p><strong>D</strong> (<em>float</em>) – Effective diameter of pipe (m)</p></li>
+<li><p><strong>D</strong> (<em>float</em>) – Hydraulic diameter of pipe (m)</p></li>
 <li><p><strong>mu</strong> (<em>float</em>) – Coolant bulk viscosity (Pa s)</p></li>
 <li><p><strong>Pr</strong> (<em>float</em>) – Coolant bulk Prandtl number</p></li>
 <li><p><strong>k</strong> (<em>float</em>) – Coolant thermal conductivity</p></li>
@@ -1651,6 +1652,25 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 </dl>
 </dd></dl>
 
+<dl class="py function">
+<dt id="bamboo.cooling.h_coolant_gnielinski">
+<code class="sig-prename descclassname"><span class="pre">bamboo.cooling.</span></code><code class="sig-name descname"><span class="pre">h_coolant_gnielinski</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rho</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">V</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Pr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.cooling.h_coolant_gnielinski" title="Permalink to this definition">¶</a></dt>
+<dd><p>Convective heat transfer coefficient for the coolant side, using Gnielinski’s correlation. Page 41 of Reference [4].</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rho</strong> (<em>float</em>) – Coolant density (kg/m3)</p></li>
+<li><p><strong>V</strong> (<em>float</em>) – Coolant velocity (m/s)</p></li>
+<li><p><strong>D</strong> (<em>float</em>) – Hydraulic diameter (m)</p></li>
+<li><p><strong>mu</strong> (<em>float</em>) – Coolant viscosity (Pa s)</p></li>
+<li><p><strong>Pr</strong> (<em>float</em>) – Prandtl number</p></li>
+<li><p><strong>k</strong> (<em>float</em>) – Coolant thermal conductivity</p></li>
+<li><p><strong>f</strong> (<em>float</em>) – Coolant friction factor</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py class">
 <dt id="bamboo.cooling.Material">
 <em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">bamboo.cooling.</span></code><code class="sig-name descname"><span class="pre">Material</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">E</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poisson</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.cooling.Material" title="Permalink to this definition">¶</a></dt>
@@ -2022,7 +2042,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 
 <dl class="py class">
 <dt id="bamboo.cooling.CoolingJacket">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">bamboo.cooling.</span></code><code class="sig-name descname"><span class="pre">CoolingJacket</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inner_wall_material</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inlet_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inlet_p0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coolant_transport</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mdot_coolant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[-</span> <span class="pre">1000,</span> <span class="pre">1000]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">configuration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spiral'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.cooling.CoolingJacket" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">bamboo.cooling.</span></code><code class="sig-name descname"><span class="pre">CoolingJacket</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inner_wall_material</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inlet_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inlet_p0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coolant_transport</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mdot_coolant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">configuration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spiral'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.cooling.CoolingJacket" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <p>Container for cooling jacket information - e.g. for regenerative cooling. All channels are assumed to have rectangular cross sections.</p>
 <dl class="field-list simple">
@@ -2033,7 +2053,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <li><p><strong>inlet_p0</strong> (<em>float</em>) – Inlet coolant stagnation pressure (Pa)</p></li>
 <li><p><strong>coolant_transport</strong> (<a class="reference internal" href="#bamboo.cooling.TransportProperties" title="bamboo.cooling.TransportProperties"><em>TransportProperties</em></a>) – Container for the coolant transport properties.</p></li>
 <li><p><strong>mdot_coolant</strong> (<em>float</em>) – Coolant mass flow rate (kg/s)</p></li>
-<li><p><strong>xs</strong> (<em>list</em>) – x positions that the cooling jacket starts and ends at, [x_min, x_max]. Defaults to [-1000, 1000].</p></li>
+<li><p><strong>xs</strong> (<em>list</em>) – x positions that the cooling jacket starts and ends at, [x_min, x_max].</p></li>
 <li><p><strong>configuration</strong> (<em>str</em><em>, </em><em>optional</em>) – Options include ‘spiral’ and ‘vertical’. Defaults to “vertical”.</p></li>
 <li><p><strong>has_ablative</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether or not the engine has an ablative.</p></li>
 </ul>
@@ -2042,12 +2062,46 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <dd class="field-even"><ul class="simple">
 <li><p><strong>blockage_ratio</strong> (<em>float</em>) – This is the proportion (by area) of the channel cross section occupied by ribs.</p></li>
 <li><p><strong>number_of_ribs</strong> (<em>int</em>) – Only relevant if ‘blockage_ratio’ !=0. This is the number of ribs present in the cooling channel. For spiral channels this is the number of ribs ‘per pitch’ - it is numerically equal to the number of parallel spiral channels.</p></li>
-<li><p><strong>channel_height</strong> (<em>float</em>) – This is the height of the channels, in the radial direction (m).</p></li>
-<li><p><strong>channel_width</strong> (<em>float</em>) – Only relevant if configuration = ‘spiral’. This is the total width (i.e. pitch) of the cooling channels (m).</p></li>
+<li><p><strong>channel_height</strong> (<em>float</em><em> or </em><em>list</em>) – This is the height of the channels, in the radial direction (m). Can be a constant (float) or list. If list, it must be an channel heights at regularly spaced x positions, which will be stretched to fit the xs input.</p></li>
+<li><p><strong>channel_width</strong> (<em>float</em>) – Only relevant if configuration = ‘spiral’. This is the total width (i.e. pitch) of the cooling channels (m). Can be a constant (float) or list. If list, it must be an channel heights at regularly spaced x positions, which will be stretched to fit the xs input.</p></li>
 <li><p><strong>outer_wall_material</strong> (<a class="reference internal" href="#bamboo.cooling.Material" title="bamboo.cooling.Material"><em>Material</em></a>) – Wall material for the outer liner.</p></li>
 </ul>
 </dd>
 </dl>
+<dl class="py method">
+<dt id="bamboo.cooling.CoolingJacket.channel_height">
+<code class="sig-name descname"><span class="pre">channel_height</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.cooling.CoolingJacket.channel_height" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get channel height at a given x postiion. Will interpolate if self.channel_height_input is a list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>float</em>) – x position in engine (m).</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>Cooling channel height (m).</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="bamboo.cooling.CoolingJacket.channel_width">
+<code class="sig-name descname"><span class="pre">channel_width</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.cooling.CoolingJacket.channel_width" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get channel width at a given x postiion. Will interpolate if self.channel_height_input is a list. Only works for spiral channels.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>float</em>) – x position in engine (m).</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>Cooling channel width (m).</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt id="bamboo.cooling.CoolingJacket.A">
 <code class="sig-name descname"><span class="pre">A</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.cooling.CoolingJacket.A" title="Permalink to this definition">¶</a></dt>
@@ -2071,7 +2125,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 <dl class="py method">
 <dt id="bamboo.cooling.CoolingJacket.D">
 <code class="sig-name descname"><span class="pre">D</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bamboo.cooling.CoolingJacket.D" title="Permalink to this definition">¶</a></dt>
-<dd><p>Get the ‘effective diameter’ of the cooling channel. This is equal 4*channel_area / wetted_channel_perimeter.</p>
+<dd><p>Hydraulic diameter of the cooling channel. This is equal 4*channel_area / wetted_channel_perimeter.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -2086,7 +2140,7 @@ <h1>Welcome to Bamboo’s documentation!<a class="headerlink" href="#welcome-to-
 </div>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>Effective diameter (m)</p>
+<dd class="field-odd"><p>Hydraulic diameter (m)</p>
 </dd>
 <dt class="field-even">Return type</dt>
 <dd class="field-even"><p>float</p>
diff --git a/docs/objects.inv b/docs/objects.inv
index 19037a9..50ab3b0 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 2b96574..9b7b821 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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diff --git a/docs/source/_autosummary/bamboo.cooling.rst b/docs/source/_autosummary/bamboo.cooling.rst
index 4a182b1..c6b96ef 100644
--- a/docs/source/_autosummary/bamboo.cooling.rst
+++ b/docs/source/_autosummary/bamboo.cooling.rst
@@ -15,6 +15,7 @@
    
       black_body
       h_coolant_dittus_boelter
+      h_coolant_gnielinski
       h_coolant_rpe
       h_coolant_sieder_tate
       h_gas_bartz