Repository: PMEAL/OpenPNM
Pull request #1795 (2020-11-29 00:46:23)

Setting quantity on phase when IC is called
Closing #1738


Patch:
diff --git a/examples/notebooks/algorithms/transient/transient_advection_diffusion.ipynb b/examples/notebooks/algorithms/transient/transient_advection_diffusion.ipynb
index 2b8e1e753..97d6cc7df 100644
--- a/examples/notebooks/algorithms/transient/transient_advection_diffusion.ipynb
+++ b/examples/notebooks/algorithms/transient/transient_advection_diffusion.ipynb
@@ -42,6 +42,40 @@
     "phys = op.physics.Standard(network=pn, phase=water, geometry=geo)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Defining Effective Pore Volume\n",
+    "\n",
+    "The accumulation of mass in the network occurs only in the pores, where the concentration is solved.  In order for mass  accumulate properly, it is necessary to assign the throat volumes to their surrounded throats.  This creates an effective pore volume.  We can define this in a custom pore-scale model, making use of the ``numpy.add.at`` function, to add 1/2 the volume of each throat to its neighboring pores.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def effective_pore_volume(target, throat_volume='throat.volume', pore_volume='pore.volume'):\n",
+    "    Pvol = geo['pore.volume']\n",
+    "    Tvol = geo['throat.volume']\n",
+    "    Vtot = Pvol.sum() + Tvol.sum()\n",
+    "    np.add.at(Pvol, pn.conns[:, 0], geo['throat.volume']/2)\n",
+    "    np.add.at(Pvol, pn.conns[:, 1], geo['throat.volume']/2)\n",
+    "    assert np.isclose(Pvol.sum(), Vtot)  # Ensure total volume has been added to Pvol\n",
+    "    return Pvol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geo.add_model(propname='pore.effective_volume', model=effective_pore_volume)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -52,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,12 +150,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Dirichlet boundery conditions and the inital conditions are next defined as follows. If the inital condition is not defined then it is assumed to be zero, so it is redundunt in this case. The boundary conditions can be defined as ``value``, ``outflow``, and ``rate``."
+    "The Dirichlet boundary conditions and the inital conditions are next defined as follows. If the inital condition is not defined then it is assumed to be zero, so it is redundunt in this case. The boundary conditions can be defined as ``value``, ``outflow``, and ``rate``."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,15 +178,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "tf = 100\n",
-    "tstep = 5\n",
+    "tstep = 10\n",
     "ad.setup(t_scheme='implicit', t_final=tf, t_output=5, t_step=tstep, t_tolerance=1e-12)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We must also tell the algorithm to use the effective pore volume rather than the default which is just 'pore.volume'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ad.settings['pore_volume'] = 'pore.effective_volume'"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -162,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -210,14 +260,14 @@
       "diffusive_conductance               throat.diffusive_conductance\n",
       "hydraulic_conductance               throat.hydraulic_conductance\n",
       "pressure                            pore.pressure\n",
-      "pore_volume                         pore.volume\n",
+      "pore_volume                         pore.effective_volume\n",
       "t_final                             100\n",
       "t_initial                           0\n",
-      "t_output                            5\n",
+      "t_output                            10\n",
       "t_precision                         12\n",
       "t_scheme                            implicit\n",
-      "t_solns                             ['5', '10', '15', '20', '25', '30', '35', '40', '45', '50', '55', '60', '65', '70', '75', '80', '85', '90', '95', '100']\n",
-      "t_step                              5\n",
+      "t_solns                             ['10', '20', '30', '40', '50', '60', '70', '80', '90', '100']\n",
+      "t_step                              10\n",
       "t_tolerance                         1e-12\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
      ]
@@ -236,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -255,25 +305,15 @@
       "5     pore.concentration@0                           1600 / 1600 \n",
       "6     pore.concentration@10                          1600 / 1600 \n",
       "7     pore.concentration@100                         1600 / 1600 \n",
-      "8     pore.concentration@15                          1600 / 1600 \n",
-      "9     pore.concentration@20                          1600 / 1600 \n",
-      "10    pore.concentration@25                          1600 / 1600 \n",
-      "11    pore.concentration@30                          1600 / 1600 \n",
-      "12    pore.concentration@35                          1600 / 1600 \n",
-      "13    pore.concentration@40                          1600 / 1600 \n",
-      "14    pore.concentration@45                          1600 / 1600 \n",
-      "15    pore.concentration@5                           1600 / 1600 \n",
-      "16    pore.concentration@50                          1600 / 1600 \n",
-      "17    pore.concentration@55                          1600 / 1600 \n",
-      "18    pore.concentration@60                          1600 / 1600 \n",
-      "19    pore.concentration@65                          1600 / 1600 \n",
-      "20    pore.concentration@70                          1600 / 1600 \n",
-      "21    pore.concentration@75                          1600 / 1600 \n",
-      "22    pore.concentration@80                          1600 / 1600 \n",
-      "23    pore.concentration@85                          1600 / 1600 \n",
-      "24    pore.concentration@90                          1600 / 1600 \n",
-      "25    pore.concentration@95                          1600 / 1600 \n",
-      "26    pore.ic                                        1600 / 1600 \n",
+      "8     pore.concentration@20                          1600 / 1600 \n",
+      "9     pore.concentration@30                          1600 / 1600 \n",
+      "10    pore.concentration@40                          1600 / 1600 \n",
+      "11    pore.concentration@50                          1600 / 1600 \n",
+      "12    pore.concentration@60                          1600 / 1600 \n",
+      "13    pore.concentration@70                          1600 / 1600 \n",
+      "14    pore.concentration@80                          1600 / 1600 \n",
+      "15    pore.concentration@90                          1600 / 1600 \n",
+      "16    pore.ic                                        1600 / 1600 \n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
       "#     Labels                                        Assigned Locations\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
@@ -297,12 +337,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x1080 with 5 Axes>"
       ]
@@ -317,8 +357,8 @@
     "# NBVAL_IGNORE_OUTPUT\n",
     "cq = ad['pore.concentration@'+str(10)]\n",
     "cw = ad['pore.concentration@'+str(20)]\n",
-    "ce = ad['pore.concentration@'+str(50)]\n",
-    "cr = ad['pore.concentration@'+str(75)]\n",
+    "ce = ad['pore.concentration@'+str(30)]\n",
+    "cr = ad['pore.concentration@'+str(40)]\n",
     "cq2d = cq.reshape((pn._shape))\n",
     "cw2d = cw.reshape((pn._shape))\n",
     "ce2d = ce.reshape((pn._shape))\n",
@@ -351,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -402,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -411,13 +451,13 @@
        "Text(0, 0.5, 'Concentration')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -434,16 +474,13 @@
     "Throats_back = pn.find_neighbor_throats(pores=Pores_back, mode='xor')\n",
     "steps = tf/tstep +1\n",
     "count = 0\n",
-    "C_ave = [0 for i in range(int(steps))]\n",
-    "td = [0 for i in range(int(steps))]\n",
-    "while count < int(steps):\n",
-    "    step = count*tstep\n",
+    "C_ave = []\n",
+    "td = ad.settings['t_solns']\n",
+    "for step in td:\n",
     "    conc_back = ad['pore.concentration@'+str(step)][Pores_back]\n",
     "    q_back = sf.rate(throats=pn.Ts,mode='single')[Throats_back]\n",
-    "    C_ave[count] = np.sum(q_back*conc_back)/np.sum(q_back)\n",
-    "    td[count] = step\n",
-    "    count = count+1\n",
-    "plt.plot(td,C_ave)\n",
+    "    C_ave.append(np.sum(q_back*conc_back)/np.sum(q_back))\n",
+    "plt.plot(td, C_ave)\n",
     "plt.gca().legend(('simulation','fitted'))\n",
     "plt.xlabel('time, s')\n",
     "plt.ylabel('Concentration')"
@@ -465,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,26 +521,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dispersion Coefficient =  3.0836E-08  m^2/s\n",
+      "Dispersion Coefficient =  2.5985E-08  m^2/s\n",
       "v_pred =  2.2569E-04  m/s\n",
-      "v_fitted =  9.0951E-05  m/s\n"
+      "v_fitted =  5.8982E-05  m/s\n"
      ]
     }
    ],
    "source": [
-    "g = [v_pred,1e-3]\n",
-    "xdata = td\n",
+    "g = [v_pred, 1e-3]\n",
+    "xdata = [float(x) for x in td]\n",
     "ydata = C_ave\n",
     "from scipy.optimize import curve_fit\n",
-    "popt, pcov = curve_fit(elution,xdata,ydata,p0=g)\n",
-    "popt\n",
+    "popt, pcov = curve_fit(elution, xdata, ydata, p0=g)\n",
     "disp_coeff = popt[1]\n",
     "v_fit = popt[0]\n",
     "print('Dispersion Coefficient = ', \"{0:.4E}\".format(disp_coeff), ' m^2/s')\n",
@@ -513,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -522,13 +558,13 @@
        "Text(0, 0.5, 'Concentration')"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Nh+qMeZsO8fQPG8k4mcGXDVfRfv8nyPEs6PBvuOoxCAh1d4iqlPCIRBAYGEhycjKRkZGaDIqRMYbk5GQCA3XYv7IkNSOHF3/axLQ1+7k5Kp7nQj8nYHc81L0WerxmDeCuVD4ekQhiYmJITEwkKSnJ3aF4nMDAQGJi9PbBsuLXrX8z6vsNBKUnsqDqD9RJWWQN5D7kW6sjOKUK4BGJwM/P76zO3pTyNqknc3h55mZ+WrWTZyPmMDhoOj4nfKHrC9bDYNoJnCqERyQCpbzZn/FHePTbtbRKX8zy8CmEZ/0NTW+Ebv8H4drTrSqaJgKlyrCpqxL57/ez+SjoM1r6bYAKTaHnZ3DZFe4OTZUhmgiUKoOMMXy0aCfb53/CjIBP8PcLhh5vQevbwcfm7vBUGaOJQKkyJs9ueGn6WmqufoX3/Odjr94eufFTCKvk7tBUGaWJQKkyJDMnjxe+nM/A3c/Qyjcec8WD+HR9QQeFUZdE/3qUKiOOZWTz3n8n8ljKa4T75cL1nyGNr3N3WMoDuLR3KRHpISLbRCReREYVsD5CRH4SkXUisklEbndlPEqVVfuPZjD1vUcZnfI0AeHR+I9YDJoEVDFx2RmBiNiAD4FuQCKwQkRmGGM259vsfmCzMeZfIhINbBORr4wx2a6KS6myZvveRA5Ovp27zHKOxPYmasgE7R5CFStXnhG0BeKNMbscFfs3QL9ztjFAmFj9QoQCKUCuC2NSqkxZt/IPAj7tQnuzikNXvkDUbV9pElDFzpXXCKoBCfnmE4HLz9lmLDADOACEATcZY+znfpGIDAeGA9SoUcMlwSpV2qyZOYEGK54h3SeUozdMo3Ljzu4OSXkoV54RFNT727ldWV4LrAWqAi2AsSIS/o8PGTPBGBNnjImLjo4u/kiVKk1ys9nyyXBarnycXf718bv3N6I1CSgXcmUiSASq55uPwTryz+92YJqxxAO7gQYujEmpUs1+LJH971xNw4QpzIsYSO3HfiGiYvWiP6jUJXBlIlgB1BWRmiLiDwzCagbKbx/QBUBEKgH1gV0ujEmpUitnxyLSP2hPRFo838S+TJeHJxAYoJ3FKddz2TUCY0yuiDwAzAVswCRjzCYRGeFYPx54CZgsIhuwmpKeNMYccVVMSpVKxpD1+3v4/foih+xVWH75RIb06qpja6gS49IHyowxs4BZ5ywbn+/9AaC7K2NQqrTLWvAyAX+8yc95l5PV+wNublff3SEpL6NPFivlRnm/vErAH2/ybV5nKgwaT+/GVdwdkvJCLn2yWCl1fmbha9h+f4NvcztB3/fpqklAuYkmAqXcYfF/kMWv811uRw52GsPANpe5OyLlxTQRKFXSFo+Bha8wNa8ja1r+Hw91refuiJSX02sESpWk38bAwpeZlteBWTWfZsJ1zfXuIOV2mgiUKim/vQm/vsx0+1V8XvFxvrq5Db42PSlX7qeJQKmS8Pvb8OtL/CwdeTfkYb67rR0hAfrvp0oH/UtUytWWvAO/vMh8W0ees9/Hd3e0IzpMnxhWpYcmAqVcacm7sOAFFgd0YmTGcL64ux21orUbaVW6aAOlUq7yx3uw4Hn+Crmau47fxbuD29CqRnl3R6XUP2giUMoV/vwA5j/HuoguDEm+g+f6Nadbo0rujkqpAmkiUKq4/TkW5o1mR3Q3rv/7Nu7pXI9h7fSBMVV6aSJQqjgt/RDmPUNClWvpkXALfVvW4PFrtRM5VbppIlCquCz9COY+TVL1HnTbO5R2dSryxoBm+sCYKvU0EShVHDZMhblPkVqzJ133DiO2YjnGDW2Nv6/+i6nST28fVepSHdoIMx4kq0obeiTcSkiQP5/d0ZbwQD93R6aUUzQRKHUpMlJgys3YA8K4+fj9pOf6MPXutlQKD3R3ZEo5Tc9blbpY9jz4/i5M6n7eLv8sa44G8PGwOOpVCnN3ZEpdEE0ESl2sha/Azl9Y3vApxu6owGPd63NF7Uh3R6XUBdOmIaUuxuYf4fe3ONpgMMPWNaJjvUju6VjL3VEpdVE0ESh1oQ5vhen3kVe1NYMSB1AuSHh7YHN8fPQ2UVU2adOQUhciMxW+GQJ+wbwc8hQ7krN5b1BLokK1N1FVdjl1RiAi0cDdQGz+zxhj7nBNWEqVQnY7TBsOx/ayqN0kPv01m5Fd6+p1AVXmOds09CPwO7AAyHNdOEqVYovfgO1zSLrqJe5dFEC7WhE8eE1dd0el1CVzNhEEG2OedGkkSpVmW2fB4tfJazqIoeuaE+RvNQnZ9LqA8gDOXiOYKSK9XBqJUqXVkR3wwz1QpTkvcjfbDqfx9sDm+tCY8hjOJoKHsZJBpoiccLyOuzIwpUqFrBPwzc1g82NBs7f4fMXfjOhUm871K7o7MqWKjVNNQ8YYfVRSeR+7HX4YAck7+LvfN4z8IYVWNcrxaPd67o5MqWLl9HMEItIX6OiYXWSMmemakJQqJZa8DVtnktv1Je76PQSbTwbvD26Jn03vulaexam/aBF5Hat5aLPj9bBjmVKeaccC+PVlaHIDr6Rcw4b9qYy5oRkx5YPdHZlSxc7ZM4JeQAtjjB1ARD4D1gCjXBWYUm6Tsgu+vwMqNWZ+3dF8+vVmbm8fS/fGld0dmVIucSHnuOXyvY9w5gMi0kNEtolIvIgUmDREpLOIrBWRTSKy+ALiUar4ZafDN0MB4WDPiTz6w3aaVotgVM8G7o5MKZdx9ozgNWCNiCwEBOtawVOFfUBEbMCHQDcgEVghIjOMMZvzbVMO+AjoYYzZJyJ6K4ZyH2Pgxwfg8GZyh3zH/bNSsBsYO6QlAb42d0enlMs4e9fQ1yKyCGiDlQieNMYcKuJjbYF4Y8wuABH5BuiHdY3hlCHANGPMPsd+Dl9Y+EoVoxUTYdM06PI8b+6szup9Oxk7pCWXRYa4OzKlXKrQpiERaeCYtgKqYB3ZJwBVHcsKU82x7SmJjmX51QPKi8giEVklIrecJ47hIrJSRFYmJSUVsVulLsLRPTD/eah9DYsrDmX84p0MubwGfZpVdXdkSrlcUWcEjwDDgbcKWGeAawr5bEHP3psC9t8a6AIEAUtFZJkxZvtZHzJmAjABIC4u7tzvUOrS2O1Wk5D4kHT1GB75dB0NKofxXJ9G7o5MqRJRaCIwxgx3vO1pjMnMv05Einq+PhGonm8+BjhQwDZHjDHpQLqI/AY0B7ajVElZNQn2/I69z3s8OCuJjOw8xg5pRaCfXhdQ3sHZu4b+dHJZfiuAuiJSU0T8gUHAjHO2+RG4SkR8RSQYuBzY4mRMSl26o3th3nNQqzPjT7Rn2a4UXr6uCXUqhro7MqVKTKFnBCJSGatdP0hEWnKmuSccKPTJGmNMrog8AMwFbMAkY8wmERnhWD/eGLNFROYA6wE7MNEYs/GSSqSUs+x2mGE1Ce2+8nXe/TSeXk0rM6B1jLsjU6pEFXWN4FrgNqxmnbfzLT8BPF3UlxtjZgGzzlk2/pz5McAYJ2JVqnit+hR2/0Ze73cYOSeZ0EBf/q9fE3dHpVSJK+oawWfAZyIywBjzfQnFpJTrHd0L860moQlpHVmXuI2xQ3TISeWdnH2O4HsR6Q00BgLzLf8/VwWmlMsYAzMeBGD3Fa/xzuQd9GxSmd5Nq7g5MKXcw9kxi8djXRO4GpgI3AAsd2FcSrnOqsmwezF5vd5i5LyjhATY+L9+TRDR0caUd3L2rqErjTG3AEeNMS8CV3D2raFKlQ3H9sG80VCzIxMzOrEu4Rj/168J0WHaJKS8l7OJ4NQzBBkiUhXIAWq6JiSlXORUk5Ax7Gn/Bm8tiKdH48r0aaZNQsq7Odvp3E+ODuLGAKuxnhD+r8uiUsoVVn8GuxZh7/UWI+ceJdjfxkvXaZOQUkUmAhHxAX4xxhwDvheRmUCgMSbV5dEpVVyOJcDc0RB7FRNPdmJtwnbeG9RCm4SUwommIcdgNG/lm8/SJKDKlNNNQnb2dPgPb86Pp3ujSvRtrh3KKQXOXyOYJyIDRM+hVVm0+nPYtRB71xd4ZN5RgvxsvNxfm4SUOsXZawSPACFArohkYnU1YYwx4S6LTKnicCwB5j4DsVfxSeY1rN63jXdvakHFsKL6TFTKezj7QFmYqwNRqtgZAz89DCaPvR3e4M3JO+jWqBL9WmiTkFL5OdU0JCK/OLNMqVJlzZew8xfsXV7gkfmpBPrZeEXvElLqH4rqfTQQ64niKBEpz9m9j+phlSq9UhNh7tNwWXsmZXdh1d5tvHNTcyqGa5OQUucqqmnoHmAkVqW/ijOJ4DjWwPRKlT6nmoTsuSR0HMOYT3fQtWFFrmtx7kipSikouvfR94D3RORBY8wHJRSTUpdm7VcQvwB7jzf497xUAnx9eKV/U20SUuo8nL1Y/IGIXAnE5v+MMeZzF8Wl1MVJ3Q9znoYaV/JpTjdW7t3KWzc2p5I2CSl1Xs72PvoFUBtYC+Q5FhtAE4EqXWY/AXnZVpPQZ9u5pkFFrm+lTUJKFcbZ5wjigEbGGOPKYJS6JDsWwNaZ2K95jkcWHMff5sNr12uTkFJFcfbJ4o1AZVcGotQlyc2GOU9ChVp8IX1Ysecoz/2rsTYJKeUEZ88IooDNIrIcyDq10BjT1yVRKXWhln0EyfH8/a8veW36Lq6uH80AbRJSyinOJoIXXBmEUpfk+AFY/B9MvZ48tDIKP9txXru+mTYJKeUkp5qGjDGLgT2An+P9CqxxCZRyv3nPgj2Xn6o+yF+7U3i6V0MqR2iTkFLOcraLibuBqcDHjkXVgOmuCkopp+1ZAhunktbmAZ5ZlEa7WhUY1EZHUVXqQjh7sfh+oD3WE8UYY3YAFV0VlFJOycuFWY9jIqoz6tA1ZOfaeV2bhJS6YM4mgixjTPapGRHxxXqOQCn3WTERDm9mdcMnmLk1lX93q0dsVIi7o1KqzHE2ESwWkaeBIBHpBnwH/OS6sJQqQloSLHyVnNjO3LOiCk2qhXNXh5rujkqpMsnZRDAKSAI2YHVENwsY7aqglCrSLy9ATjrv+N7J0ZM5vDGgGb42Z/+clVL5OXv7aBAwyRjzXwARsTmWZbgqMKXOK3ElrPmSxIZ389EaG/d2rkXjqhHujkqpMsvZQ6hfsCr+U4KABcUfjlJFsOfBz49iQitz555rqBkVwsNd6ro7KqXKNGcTQaAxJu3UjON9sGtCUqoQa76Ag2v5seIIth01vHZ9UwL9bO6OSqkyzdlEkC4irU7NiEhr4GRRHxKRHiKyTUTiRWRUIdu1EZE8EbnByXiUN8pIgQUvklapLY9sqcvgtjVoVyvS3VEpVeY5e41gJPCdiBxwzFcBbirsA47rCB8C3YBEYIWIzDDGbC5guzeAuRcSuPJCC1/BZB7jcd+hRIcF8lSvBu6OSCmP4OzANCtEpAFQH2u4yq3GmJwiPtYWiDfG7AIQkW+AfsDmc7Z7EPgeaHMhgSsvc3A9rJzEhqo3MntnFBOGNSE80M/dUSnlEZw9IwCroo51fKaliBQ1Qlk1ICHffCJwef4NRKQa0B+4Bk0E6nyMgVmPkxdYntv3dqd30yp0b6y9oitVXFw5QllBz/mf+zTyu8CTxpi8wroFEJHhwHCAGjVqOBOy8iTrv4WEZXwcPpJcv3Ce79vI3REp5VFcOUJZIpC/968Y4MA528QB3ziSQBTQS0RyjTFndWhnjJkATACIi4vTri28SeZxmP8sRyKaMObvON64oSEVw7RnUaWKk7OJ4NQIZQcv4LtXAHVFpCawHxgEDMm/gTHmdJ8AIjIZmHluElBebvEbmLTDPGAepn2ditzYOsbdESnlcVw2QpkxJldEHsC6G8iG9WTyJhEZ4Vg//uLDVl4haRvmr/H8HtqDdam1mdtfxx9WyhVcOkKZMWYWVr9E+ZcVmACMMbddzD6UhzIGZj9Bji2YkUf68WjvetSI1GcYlXIFZ28fXSwilThzZ89yY8xh14WlvN6WGbBrEW/LnVSPqc7t7bVnUaVcxdkRygYCy4EbgYHAX/oUsHKZ7AyY8zT7A+rwadbVvD6gGTYfbRJSylWcbRp6Bmhz6ixARKKxOp2b6qrAlBdb8jYcT+ThrOe4u3M9GlYJd3dESnk0ZxOBzzlNQck430+RUs5L2Y35433m+XQkJao1D1xTx90RKYd9TTIAABJ/SURBVOXxnE0Ec0RkLvC1Y/4mzrkIrFSxmDeaHOPDsxk38eEtzbRnUaVKQKGJQETqAJWMMY+LyPVAB6wnhpcCX5VAfMqb7PwVts7kndxB9LiiBW1iK7g7IqW8QlFnBO8CTwMYY6YB0wBEJM6x7l8ujU55j7wc7LOe5IBUZkH4AH7sqT2LKlVSimrnjzXGrD93oTFmJVYHdEoVj+X/xSd5Oy9k38zrN8UR7H8h/SEqpS5FUf9thXXqElTIOqWcl5ZEzq+v8kdec+q0v5HWl2mTkFIlqagzghUicve5C0XkTmCVa0JS3iZr3gtITgZflruHf3ev5+5wlPI6RZ0RjAR+EJGbOVPxxwH+WOMIKHVpDqzBb/1XTMrrxb8H9yHAV+8SUqqkFZoIjDF/A1eKyNVAE8fin40xv7o8MuX5jCFl6kjyTBj2qx6ncdUId0eklFdytq+hhcBCF8eivEzqX19RIWUt74eP5L4uzd0djlJeS58OVm5hMo9jn/8s601t+gx7FF+b/ikq5S7636fcYsu3L1A+L4WEts9Tq6L2JaSUO2kiUCXu4K5N1Nn5Gb8FdaVnz/OObaSUKiGaCFSJstsN+6f8mxx8qTv0LXy0e2ml3E4TgSpR82Z8SVzWX8Q3uo8q1WLdHY5SCk0EqgTtOJBMvTWvcsi3Gs0GjHJ3OEopB00EqkTk5Nn57ctXqCUHCPrXGMQ3wN0hKaUcNBGoEjFpzjIGpv+Pw5U7EdG8t7vDUUrlo108Kpdbn3iMCsteJ8iWQ9gNb7s7HKXUOfSMQLlUZk4eH3/9HTfaFpPXdgRE6dCTSpU2mgiUS705Zwt3nRhPVmA0Adc86e5wlFIF0KYh5TLLdiVzdNkXtPSLhx7jIFCfIFaqNNJEoFwiLSuX575dytf+U8ir0hpbs0HuDkkpdR7aNKRc4pWfNzMg7WsizVFsvcaAj/6pKVVa6X+nKnbfr0pk2Yrl3OU3B1oMhZjW7g5JKVUIbRpSxWptwjGe+mE9U8Kn4EMQdH3e3SEppYqgZwSq2Bw+nsk9X6xkSNAKWmYtRzqNgtCK7g5LKVUETQSqWGTl5nHPl6sIOJnEsz6fQEwbaHevu8NSSjnBpYlARHqIyDYRiReRf/QyJiI3i8h6x+tPEdHxCssgYwyjf9jImn1H+S5mCra8LLhuHPjoQPRKlQUuSwQiYgM+BHoCjYDBItLonM12A52MMc2Al4AJropHuc5nf+7hu1WJjGuynUoHF0KX5yCqrrvDUko5yZVnBG2BeGPMLmNMNvAN0C//BsaYP40xRx2zy4AYF8ajXODP+CO89PMWbqzrQ4+Ed6DGFXD5CHeHpZS6AK5MBNWAhHzziY5l53MnMLugFSIyXERWisjKpKSkYgxRXYqElAzu+99qakUG85rffxF7LvT7UJuElCpjXJkIChqD0BS4ocjVWImgwM5ojDETjDFxxpi46OjoYgxRXaz0rFzu/nwldrvh6zY78N31C3R9ESJruzs0pdQFcuVzBIlA9XzzMcCBczcSkWbARKCnMSbZhfGoYmK3Gx77bh3b/z7B1wOrETX7Toi9Ctrc5e7QlFIXwZVnBCuAuiJSU0T8gUHAjPwbiEgNYBowzBiz3YWxqGI0dmE8szce4umeDbh8/XOAgX5jtRsJpcool50RGGNyReQBYC5gAyYZYzaJyAjH+vHAc0Ak8JGIAOQaY+JcFZO6dPM2HeLt+du5vmU17gxaBLsXQ593oHysu0NTSl0kMabAZvtSKy4uzqxcudLdYXil7X+foP+Hf1CnYijfDqxCwH+vguptYNh0kIIuCSmlSgsRWXW+A23ta0g55VhGNnd/vpLgAF8+HtqKgB9utO4O6jtWk4BSZZwmAlWk3Dw7D369hoPHMvl6eDsqb/sC9i6xkkC56kV/gVKqVNNEoIr0+uyt/L7jCP+5oRmtQ1Ng/vNQpxu0HOru0JRSxUATgSrU96sSmbhkN7ddGcvAVlXh017g6w9939cmIaU8hCYCdV7W2AIbuLJ2JM/0bgjLPoSEZdD/Ywiv6u7wlFLFRG/8VgVKSMngni9WUik8gA+HtMIvJR5+eQnq94JmN7k7PKVUMdJEoP5h84HjXD/uTzJz7EwYFkf5QB+YPgL8g6HPu9okpJSH0USgzrJ0ZzI3fbwUXx9h6ograFglHP58H/avgl5vQlgld4eolCpmeo1AnTZrw0FGfrOWyyKD+eyOtlQtFwR/b4ZFr0HDvtBkgLtDVEq5gCYCBcDnS/fw/IxNtK5Rnom3xlEu2B/ycmD6vRAQBr3f1iYhpTyUJgIvZ4zhrXnbGbswnq4NKzF2SEsC/RzjCSx5Bw6uhYGfQ6h2/62Up9JE4MVy8+w8/cMGvl2ZyOC21XmpXxN8bY7LRrt/h8VvWM1BjfoV/kVKqTJNE4GXOpmdxwP/W80vWw/z0DV1+He3esippp9di+B/gyCyjnWBWCnl0TQReKGj6dnc+dkK1iQc46XrmjCs3WVnVu78Fb4eDBVqwy0/QnAF9wWqlCoRmgi8zP5jJ7l10nL2pWQw7uZW9GhS5czK+AXw9RCIqgu3zICQSPcFqpQqMZoIvMi2Qye4ddJy0rNy+fyOtrSrla+i3z4PpgyF6HpWEtAzAaW8hiYCL7F8dwp3fbaCQD8b3556UOyUbXPg22FQsREM+0GTgFJeRhOBF5i76RAPfr2GmPJBfH5HW2LKB59ZufVn+PZWqNwUhk2DoPLuC1Qp5RaaCDzc//7ax+jpG2gWU45Jt7WhQoj/mZVbfoLvboMqLWDo9xBUzm1xKqXcRxOBhzqUmsmb87YxdVUiV9eP5sObWxHsn+/XvWk6TL0DqrW2kkBg+Pm/TCnl0TQReJi0rFwmLN7JhN93YbfDPZ1q8Vj3+vjZ8vUvuPF7+P5uiGkDQ6daXUgopbyWJgIPkZtnZ8rKBN6Zv4MjaVn0bV6Vx6+tT/UKwWdvuP47+GE4VG8HN3+rSUAppYmgrDPGsHDbYV6dtZX4w2m0ja3AxFvjaFG9gPb+dVOscQUuaw9DpoB/SMkHrJQqdTQRlGEb96fyys9bWLormZpRIXw8rDXdG1U601VEfmv/B9Pvg5pXweAp1iAzSimFJoIy6cCxk7w5dxvT1uynQog/L/ZtzJDLa5x9HSC/1V/AjAehVicY9LUmAaXUWTQRlCEnMnMYt2gnnyzZjQHu7VybezvXJjzQ7/wfWjUZfnoYaneBQV+BX1BJhauUKiM0EZQBOXl2vlm+j3cX7CA5PZv+LavxaPd6Zz8Ylp8x1tCSKyfB2q+gbncY+AX4BZZs4EqpMkETQSmWkJLB7zuOMHHJLnYlpdOuVgUm92pE05iIgj+QmQrrv7XOAv7eCH4h0HY4dH8ZfANKNHalVNmhiaAUSc3I4c+dR1gSf4Q/4o+wJzkDgDoVQ5l4SxxdGlb854VgY2D/alg1CTZOg5wMqNwM+rwDTW7QB8WUUkXSROBGWbl5rNp7lCU7rIp/w/5U7AZC/G20qxXJrVfG0qFOFHUqhv4zAWQehw3fwapP4dAG8Au2RhOLux2qttLxhZVSTtNEUILsdsOWQ8f5I/4IS+KTWb47mcwcOzYfoWX1cjx4TV061I2iRfVy578DaP9qq/Lf8D3kpEOlptD7LWg6UI/+lVIXxaWJQER6AO8BNmCiMeb1c9aLY30vIAO4zRiz2pUxuZoxhuMnc0lKy+KI45V0IovV+47xZ/wRktOzAau5Z1CbGnSoE8XltSoQVtidP1knHEf/k+HgOsfR//XQ+g6opkf/SqlL47JEICI24EOgG5AIrBCRGcaYzfk26wnUdbwuB8Y5pm5ljCHPbsi1n5nm5Nk5mp7tqOCzOXLiTEV/JC3bmp6w3mfn2f/xndFhAXSsF02HWuXoUN2fSv5ZkHUcMrfB7uPWhd4sxzTz1HyqNd2/GrLToFITawzhZgMh8DwXjJVS6gK58oygLRBvjNkFICLfAP2A/ImgH/C5McYAy0SknIhUMcYcLO5g1i36nojfnges66vG8cZw6v3Zy8516pg72vESDAjYRPD1EWw+jmmY9d4mWFMfwdcHfHIzkfhU2JJedLB+IVZFHxhuTRv3h1a3QkycHv0rpYqdKxNBNSAh33wi/zzaL2ibasBZiUBEhgPDAWrUqHFRwQSERJASXAvBqktFBB/BMS9nluWfJ992IvjZfAjwtRHg54O/rw/+Nts59fI5lXT+lX5BEBBxpoIPCD+7sg/IN7XppRulVMlxZY1T0KHruQfbzmyDMWYCMAEgLi6uoAP2IjVo0xXadL2YjyqllEc7z60pxSIRqJ5vPgY4cBHbKKWUciFXJoIVQF0RqSki/sAgYMY528wAbhFLOyDVFdcHlFJKnZ/LmoaMMbki8gAwF+v20UnGmE0iMsKxfjwwC+vW0Xis20dvd1U8SimlCubSq5LGmFlYlX3+ZePzvTfA/a6MQSmlVOFc2TSklFKqDNBEoJRSXk4TgVJKeTlNBEop5eXEul5bdohIErD3Ij8eBRwpxnDKAi2zd9Aye4dLKfNlxpjoglaUuURwKURkpTEmzt1xlCQts3fQMnsHV5VZm4aUUsrLaSJQSikv522JYIK7A3ADLbN30DJ7B5eU2auuESillPonbzsjUEopdQ5NBEop5eW8JhGISA8R2SYi8SIyyt3xuIKIVBeRhSKyRUQ2icjDjuUVRGS+iOxwTMu7O9biJCI2EVkjIjMd855e3nIiMlVEtjp+11d4QZn/7fib3igiX4tIoKeVWUQmichhEdmYb9l5yygiTznqs20icu2l7NsrEoGI2IAPgZ5AI2CwiDRyb1QukQs8aoxpCLQD7neUcxTwizGmLvCLY96TPAxsyTfv6eV9D5hjjGkANMcqu8eWWUSqAQ8BccaYJljd2g/C88o8GehxzrICy+j4vx4ENHZ85iNHPXdRvCIRAG2BeGPMLmNMNvAN0M/NMRU7Y8xBY8xqx/sTWBVENayyfubY7DPgOvdEWPxEJAboDUzMt9iTyxsOdAQ+ATDGZBtjjuHBZXbwBYJExBcIxhrJ0KPKbIz5DUg5Z/H5ytgP+MYYk2WM2Y01pkvbi923tySCakBCvvlExzKPJSKxQEvgL6DSqZHfHNOK7ous2L0LPAHY8y3z5PLWApKATx3NYRNFJAQPLrMxZj/wJrAPOIg1kuE8PLjM+ZyvjMVap3lLIpAClnnsfbMiEgp8D4w0xhx3dzyuIiJ9gMPGmFXujqUE+QKtgHHGmJZAOmW/SaRQjnbxfkBNoCoQIiJD3RuV2xVrneYtiSARqJ5vPgbr1NLjiIgfVhL4yhgzzbH4bxGp4lhfBTjsrviKWXugr4jswWruu0ZEvsRzywvW33KiMeYvx/xUrMTgyWXuCuw2xiQZY3KAacCVeHaZTzlfGYu1TvOWRLACqCsiNUXEH+siyww3x1TsRESw2o63GGPezrdqBnCr4/2twI8lHZsrGGOeMsbEGGNisX6nvxpjhuKh5QUwxhwCEkSkvmNRF2AzHlxmrCahdiIS7Pgb74J1/cuTy3zK+co4AxgkIgEiUhOoCyy/6L0YY7ziBfQCtgM7gWfcHY+LytgB6/RwPbDW8eoFRGLdcbDDMa3g7lhdUPbOwEzHe48uL9ACWOn4PU8HyntBmV8EtgIbgS+AAE8rM/A11jWQHKwj/jsLKyPwjKM+2wb0vJR9axcTSinl5bylaUgppdR5aCJQSikvp4lAKaW8nCYCpZTycpoIlFLKy2kiUMrB0avnffnmq4rIVHfGpFRJ0NtHlXJw9M8001g9XCrlNfSMQKkzXgdqi8haERkjIrGn+oYXkdtEZLqI/CQiu0XkARF5xNHx2zIRqeDYrraIzBGRVSLyu4g0KGyHItJYRJY79rleROqWQDmVOouvuwNQqhQZBTQxxrSA02cI+TXB6tE1EKvb3yeNMS1F5B3gFqyeUCcAI4wxO0TkcuAj4JpC9jkCeM8Y85Wj+5OL7lNeqYuliUAp5y001jgPJ0QkFfjJsXwD0MzR6+uVwHdWlziA1RVCYZYCzzjGVZhmjNnhgriVKpQ2DSnlvKx87+355u1YB1U+wDFjTIt8r4aFfaEx5n9AX+AkMFdECjt7UMolNBEodcYJIOxiP2yssR92i8iNYPUGKyLNHe/7i8hr535GRGoBu4wx72P1KNnsYvev1MXSRKCUgzEmGfjDMUD6mIv8mpuBO0VkHbCJM0Oi1gYKGiToJmCjiKwFGgCfX+R+lbpoevuoUiXAMWDOv40xSe6ORalzaSJQSikvp01DSinl5TQRKKWUl9NEoJRSXk4TgVJKeTlNBEop5eU0ESillJf7fzl+3weI/ykgAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -543,7 +579,7 @@
     "# NBVAL_IGNORE_OUTPUT\n",
     "el = [0 for i in range(len(ydata))]\n",
     "for i in range(len(ydata)):\n",
-    "    el[i] = elution(xdata[i],popt[0],popt[1])\n",
+    "    el[i] = elution(xdata[i], popt[0], popt[1])\n",
     "plt.plot(xdata, ydata, xdata, el)\n",
     "plt.gca().legend(('simulation','fitted'))\n",
     "plt.xlabel('time, s')\n",
@@ -571,5 +607,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/notebooks/algorithms/transient/transient_fickian_diffusion.ipynb b/examples/notebooks/algorithms/transient/transient_fickian_diffusion.ipynb
index 6e2b15eb9..4ec60ac30 100644
--- a/examples/notebooks/algorithms/transient/transient_fickian_diffusion.ipynb
+++ b/examples/notebooks/algorithms/transient/transient_fickian_diffusion.ipynb
@@ -395,60 +395,60 @@
     {
      "data": {
       "text/plain": [
-       "array([0.2    , 0.20578, 0.21248, 0.21467, 0.21815, 0.22649, 0.23687,\n",
-       "       0.24519, 0.25411, 0.26817, 0.28043, 0.28309, 0.29757, 0.32005,\n",
-       "       0.32691, 0.33485, 0.3435 , 0.35934, 0.3735 , 0.37873, 0.38647,\n",
-       "       0.40779, 0.43106, 0.4425 , 0.45428, 0.46841, 0.48092, 0.49113,\n",
-       "       0.5    , 0.2    , 0.20524, 0.21101, 0.21442, 0.21814, 0.22632,\n",
-       "       0.23703, 0.24672, 0.25496, 0.26728, 0.28003, 0.28484, 0.29302,\n",
-       "       0.3123 , 0.3256 , 0.33476, 0.34491, 0.36002, 0.37387, 0.38015,\n",
-       "       0.39123, 0.40737, 0.42695, 0.44133, 0.45332, 0.47001, 0.48166,\n",
-       "       0.48864, 0.5    , 0.2    , 0.20603, 0.21195, 0.21491, 0.21913,\n",
-       "       0.22404, 0.23777, 0.24816, 0.25685, 0.26726, 0.2763 , 0.28314,\n",
-       "       0.29524, 0.31127, 0.32187, 0.33432, 0.34616, 0.36352, 0.3766 ,\n",
-       "       0.38282, 0.39108, 0.40795, 0.42554, 0.43949, 0.45043, 0.46406,\n",
-       "       0.47604, 0.48931, 0.5    , 0.2    , 0.20768, 0.21185, 0.21528,\n",
-       "       0.22037, 0.23023, 0.24197, 0.24906, 0.25767, 0.26418, 0.27261,\n",
-       "       0.28368, 0.29572, 0.30781, 0.31585, 0.3336 , 0.35481, 0.36762,\n",
-       "       0.37579, 0.38162, 0.39376, 0.41072, 0.42533, 0.44136, 0.45347,\n",
-       "       0.46213, 0.47439, 0.48978, 0.5    , 0.2    , 0.20777, 0.21292,\n",
-       "       0.21646, 0.22359, 0.23143, 0.24108, 0.25014, 0.25569, 0.26253,\n",
-       "       0.27314, 0.28284, 0.29127, 0.30443, 0.31951, 0.33251, 0.351  ,\n",
-       "       0.36843, 0.37878, 0.38405, 0.39349, 0.40392, 0.42682, 0.44638,\n",
-       "       0.45458, 0.46478, 0.47726, 0.49075, 0.5    , 0.2    , 0.20695,\n",
-       "       0.21402, 0.21744, 0.22539, 0.23288, 0.24073, 0.24887, 0.25339,\n",
-       "       0.26385, 0.2759 , 0.28089, 0.28802, 0.30091, 0.31717, 0.32889,\n",
-       "       0.34786, 0.36573, 0.38047, 0.38662, 0.39522, 0.40675, 0.42803,\n",
-       "       0.44495, 0.45451, 0.46641, 0.47713, 0.48987, 0.5    , 0.2    ,\n",
-       "       0.20689, 0.21457, 0.2207 , 0.22838, 0.23381, 0.24009, 0.24605,\n",
-       "       0.25324, 0.26354, 0.27548, 0.279  , 0.28682, 0.29672, 0.31459,\n",
-       "       0.33008, 0.3482 , 0.36404, 0.37724, 0.38851, 0.39883, 0.41255,\n",
-       "       0.42942, 0.44039, 0.45099, 0.46598, 0.47961, 0.48808, 0.5    ,\n",
-       "       0.2    , 0.20555, 0.21413, 0.22316, 0.22751, 0.23366, 0.23938,\n",
-       "       0.24714, 0.25625, 0.25966, 0.26992, 0.27743, 0.28521, 0.30055,\n",
-       "       0.31519, 0.3313 , 0.34844, 0.36621, 0.37828, 0.38629, 0.40142,\n",
-       "       0.41408, 0.42221, 0.43824, 0.45319, 0.46454, 0.47702, 0.48827,\n",
-       "       0.5    , 0.2    , 0.20603, 0.21155, 0.21993, 0.2282 , 0.23456,\n",
-       "       0.2412 , 0.24616, 0.2536 , 0.26054, 0.26776, 0.27494, 0.28961,\n",
-       "       0.30559, 0.31269, 0.33128, 0.34791, 0.36172, 0.37826, 0.39045,\n",
-       "       0.40436, 0.41337, 0.42006, 0.43379, 0.45064, 0.46394, 0.47489,\n",
-       "       0.48396, 0.5    , 0.2    , 0.20815, 0.21293, 0.21718, 0.225  ,\n",
-       "       0.23381, 0.24254, 0.24653, 0.25293, 0.25908, 0.26692, 0.27348,\n",
-       "       0.28964, 0.30557, 0.31867, 0.33034, 0.34511, 0.35778, 0.37557,\n",
-       "       0.39162, 0.40388, 0.41499, 0.41981, 0.43327, 0.44932, 0.45823,\n",
-       "       0.47055, 0.48617, 0.5    , 0.2    , 0.20739, 0.21275, 0.21975,\n",
-       "       0.2259 , 0.23101, 0.23932, 0.24469, 0.25229, 0.26155, 0.26788,\n",
-       "       0.27806, 0.29607, 0.30713, 0.31753, 0.32895, 0.34481, 0.36093,\n",
-       "       0.3753 , 0.38835, 0.40206, 0.41465, 0.41981, 0.43602, 0.4521 ,\n",
-       "       0.45706, 0.46494, 0.48449, 0.5    , 0.2    , 0.20639, 0.21074,\n",
-       "       0.2172 , 0.22432, 0.23017, 0.2403 , 0.24691, 0.25134, 0.26147,\n",
-       "       0.26912, 0.27448, 0.29249, 0.31029, 0.3213 , 0.33052, 0.34299,\n",
-       "       0.35492, 0.37287, 0.39198, 0.40188, 0.41225, 0.42283, 0.4375 ,\n",
-       "       0.44954, 0.45838, 0.46947, 0.48533, 0.5    , 0.2    , 0.20521,\n",
-       "       0.20934, 0.21327, 0.22147, 0.22692, 0.23729, 0.24775, 0.25546,\n",
-       "       0.26572, 0.26834, 0.27299, 0.29058, 0.30984, 0.32287, 0.32736,\n",
-       "       0.34244, 0.35939, 0.3695 , 0.3879 , 0.40407, 0.41119, 0.42389,\n",
-       "       0.43887, 0.44932, 0.45992, 0.47402, 0.4918 , 0.5    ])"
+       "array([0.2    , 0.20613, 0.21323, 0.21554, 0.21919, 0.22793, 0.23878,\n",
+       "       0.24746, 0.25667, 0.27111, 0.28368, 0.28639, 0.30093, 0.32348,\n",
+       "       0.33034, 0.33826, 0.34688, 0.36263, 0.37669, 0.38178, 0.38917,\n",
+       "       0.40928, 0.43137, 0.44306, 0.4589 , 0.47865, 0.48876, 0.43965,\n",
+       "       0.5    , 0.2    , 0.20556, 0.21167, 0.21528, 0.21919, 0.22776,\n",
+       "       0.23895, 0.24904, 0.25759, 0.27021, 0.28326, 0.28813, 0.29637,\n",
+       "       0.3157 , 0.32903, 0.33816, 0.34828, 0.3633 , 0.37707, 0.38317,\n",
+       "       0.39372, 0.40894, 0.4274 , 0.44127, 0.45753, 0.4818 , 0.49549,\n",
+       "       0.47686, 0.5    , 0.2    , 0.20639, 0.21266, 0.2158 , 0.22023,\n",
+       "       0.22538, 0.23973, 0.25056, 0.25952, 0.27021, 0.27944, 0.28639,\n",
+       "       0.29856, 0.31465, 0.32527, 0.33771, 0.3495 , 0.36677, 0.37975,\n",
+       "       0.3858 , 0.39371, 0.40953, 0.42606, 0.43985, 0.45239, 0.47136,\n",
+       "       0.48329, 0.4212 , 0.5    , 0.2    , 0.20814, 0.21256, 0.21618,\n",
+       "       0.22154, 0.23188, 0.24417, 0.2515 , 0.26039, 0.26707, 0.27566,\n",
+       "       0.28689, 0.29902, 0.31117, 0.31923, 0.33695, 0.3581 , 0.37085,\n",
+       "       0.37895, 0.38467, 0.39625, 0.41221, 0.4259 , 0.44188, 0.45602,\n",
+       "       0.46786, 0.4753 , 0.41696, 0.5    , 0.2    , 0.20824, 0.21369,\n",
+       "       0.21743, 0.22492, 0.23317, 0.24323, 0.25264, 0.25837, 0.26538,\n",
+       "       0.2762 , 0.28604, 0.29454, 0.30775, 0.32285, 0.33583, 0.35427,\n",
+       "       0.37163, 0.38191, 0.38707, 0.39613, 0.40598, 0.42754, 0.44635,\n",
+       "       0.4577 , 0.47283, 0.48495, 0.46276, 0.5    , 0.2    , 0.20736,\n",
+       "       0.21485, 0.21846, 0.22682, 0.23469, 0.24287, 0.25134, 0.25601,\n",
+       "       0.26672, 0.27904, 0.28408, 0.29127, 0.30419, 0.32048, 0.33219,\n",
+       "       0.35111, 0.36891, 0.38355, 0.38959, 0.39785, 0.40874, 0.42881,\n",
+       "       0.44526, 0.45816, 0.47598, 0.48713, 0.44271, 0.5    , 0.2    ,\n",
+       "       0.2073 , 0.21544, 0.22189, 0.22997, 0.23566, 0.24223, 0.24843,\n",
+       "       0.25585, 0.26642, 0.27862, 0.28218, 0.29005, 0.29999, 0.31788,\n",
+       "       0.33336, 0.35142, 0.36719, 0.38029, 0.39138, 0.40137, 0.41435,\n",
+       "       0.43007, 0.44058, 0.45304, 0.47519, 0.4922 , 0.46775, 0.5    ,\n",
+       "       0.2    , 0.20588, 0.21496, 0.22449, 0.22907, 0.23551, 0.24149,\n",
+       "       0.24955, 0.25898, 0.26248, 0.27295, 0.28058, 0.28842, 0.30383,\n",
+       "       0.31849, 0.33458, 0.35166, 0.36934, 0.38134, 0.38922, 0.40394,\n",
+       "       0.41612, 0.42363, 0.43906, 0.45412, 0.47179, 0.4872 , 0.43361,\n",
+       "       0.5    , 0.2    , 0.20639, 0.21223, 0.22108, 0.22979, 0.23645,\n",
+       "       0.24339, 0.24856, 0.25624, 0.26337, 0.27075, 0.27803, 0.29283,\n",
+       "       0.30891, 0.31601, 0.33456, 0.35114, 0.36487, 0.38131, 0.39331,\n",
+       "       0.40691, 0.41549, 0.4218 , 0.43526, 0.45311, 0.47096, 0.48569,\n",
+       "       0.46876, 0.5    , 0.2    , 0.20864, 0.2137 , 0.2182 , 0.22643,\n",
+       "       0.23566, 0.24479, 0.24894, 0.25554, 0.26187, 0.26988, 0.27655,\n",
+       "       0.29285, 0.30886, 0.32199, 0.33364, 0.34835, 0.36095, 0.37859,\n",
+       "       0.39445, 0.40637, 0.41703, 0.42156, 0.43457, 0.45093, 0.46137,\n",
+       "       0.47323, 0.45932, 0.5    , 0.2    , 0.20784, 0.21351, 0.2209 ,\n",
+       "       0.22738, 0.23274, 0.24143, 0.24703, 0.25489, 0.2644 , 0.27087,\n",
+       "       0.28117, 0.29933, 0.31044, 0.32087, 0.33226, 0.34805, 0.36407,\n",
+       "       0.37832, 0.39121, 0.40454, 0.41665, 0.42153, 0.43736, 0.45351,\n",
+       "       0.46035, 0.47078, 0.42003, 0.5    , 0.2    , 0.20678, 0.21139,\n",
+       "       0.21822, 0.22574, 0.23187, 0.24245, 0.24934, 0.25394, 0.26433,\n",
+       "       0.27215, 0.27757, 0.29571, 0.3136 , 0.32463, 0.33384, 0.34625,\n",
+       "       0.3581 , 0.37588, 0.39474, 0.40438, 0.4143 , 0.42443, 0.43903,\n",
+       "       0.45166, 0.46521, 0.4777 , 0.4507 , 0.5    , 0.2    , 0.20553,\n",
+       "       0.20991, 0.21407, 0.22274, 0.22849, 0.2393 , 0.25019, 0.25816,\n",
+       "       0.26872, 0.27138, 0.27608, 0.29378, 0.31313, 0.32622, 0.3307 ,\n",
+       "       0.3457 , 0.36254, 0.37257, 0.39066, 0.40646, 0.4133 , 0.4257 ,\n",
+       "       0.44102, 0.45398, 0.46978, 0.47805, 0.43753, 0.5    ])"
       ]
      },
      "execution_count": 16,
@@ -491,7 +491,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x432 with 2 Axes>"
       ]
@@ -534,7 +534,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/openpnm/algorithms/GenericTransport.py b/openpnm/algorithms/GenericTransport.py
index e00a36092..692c0f458 100644
--- a/openpnm/algorithms/GenericTransport.py
+++ b/openpnm/algorithms/GenericTransport.py
@@ -551,6 +551,7 @@ class GenericTransport(GenericAlgorithm):
         """
         logger.info('―' * 80)
         logger.info('Running GenericTransport')
+        self._validate_settings()
         x0 = np.zeros_like(self.b) if x0 is None else x0
         self["pore.initial_guess"] = x0
         self._run_generic(x0)
@@ -753,6 +754,12 @@ class GenericTransport(GenericAlgorithm):
         flag_converged = True if res <= res_tol else False
         return flag_converged
 
+    def _validate_settings(self):
+        if self.settings['quantity'] is None:
+            raise Exception('"quantity" has not been defined on this algorithm')
+        if self.settings['conductance'] is None:
+            raise Exception('"conductance" has not been defined on this algorithm')
+
     def _validate_data_health(self):
         r"""
         Check whether A and b are well-defined, i.e. doesn't contain nans.
diff --git a/openpnm/algorithms/ReactiveTransport.py b/openpnm/algorithms/ReactiveTransport.py
index 5b495bc99..552744ece 100644
--- a/openpnm/algorithms/ReactiveTransport.py
+++ b/openpnm/algorithms/ReactiveTransport.py
@@ -152,6 +152,7 @@ class ReactiveTransport(GenericTransport):
         x0 : ND-array
             Initial guess of unknown variable
         """
+        self._validate_settings()
         quantity = self.settings['quantity']
         logger.info('Running ReactiveTransport')
         x0 = np.zeros(self.Np, dtype=float) if x0 is None else x0
@@ -242,15 +243,17 @@ class ReactiveTransport(GenericTransport):
         phase = self.project.phases()[self.settings['phase']]
         physics = self.project.find_physics(phase=phase)
         geometries = self.project.geometries().values()
-        # Put quantity on phase so physics finds it when regenerating
-        phase.update(self.results())
         # Regenerate iterative props with new guess
         iterative_props = self._get_iterative_props()
-        phase.regenerate_models(propnames=iterative_props)
-        for geometry in geometries:
-            geometry.regenerate_models(iterative_props)
-        for phys in physics:
-            phys.regenerate_models(iterative_props)
+        if len(iterative_props) > 0:
+            # Put quantity on phase so physics finds it when regenerating
+            key = self.settings['quantity']
+            phase[key] = self[key]
+            phase.regenerate_models(propnames=iterative_props)
+            for geometry in geometries:
+                geometry.regenerate_models(iterative_props)
+            for phys in physics:
+                phys.regenerate_models(iterative_props)
 
     def _apply_sources(self):
         """r
@@ -398,7 +401,10 @@ class ReactiveTransport(GenericTransport):
             return []
         iterative_props = list(nx.dag.lexicographical_topological_sort(dg))
         # "quantity" shouldn't be in the returned list but "variable_props" should
-        iterative_props.remove(self.settings["quantity"])
+        try:
+            iterative_props.remove(self.settings["quantity"])
+        except ValueError:
+            pass
         return iterative_props
 
     @docstr.dedent
diff --git a/openpnm/algorithms/TransientReactiveTransport.py b/openpnm/algorithms/TransientReactiveTransport.py
index fc11b6391..ff62996e0 100644
--- a/openpnm/algorithms/TransientReactiveTransport.py
+++ b/openpnm/algorithms/TransientReactiveTransport.py
@@ -47,9 +47,9 @@ class TransientReactiveTransportSettings(GenericSettings):
         is not a multiple of 't_step', 't_output' will be approximated.
         When 't_output' is a list or ND-array, transient solutions
         corresponding to this list or array will be stored.
-    output_times : list
-        List of output times. The values in the list must be multiples of
-        the time step 't_step'.
+    t_solns : list
+        List of output times at which a solution was written to the
+        dictionary.  Can be used to iterate over the results.
     t_tolerance : scalar
         Transient solver tolerance. The simulation stops (before reaching
         't_final') when the residual falls below 't_tolerance'. The
@@ -172,15 +172,24 @@ class TransientReactiveTransport(ReactiveTransport):
             imposed to all pores.
 
         """
-        quantity = self.settings['quantity']
-        values = np.array(values) * 1.0
+        values = np.ones([self.Np, ]) * values
         if values.size > 1 and values.size != self.Np:
             raise Exception('The number of initial values must be either 1 or Np')
         self['pore.ic'] = values
+        quantity = self.settings['quantity']
         if not quantity:
             raise Exception('"quantity" has not been defined on this algorithm')
         self[quantity] = values
 
+    def _overwrite_ICs_with_value_BCs(self):
+        ic_vals = self['pore.ic']
+        # Ensure the given initial conditions have any value BC inserted
+        bc_pores = ~np.isnan(self['pore.bc_value'])
+        ic_vals[bc_pores] = self['pore.bc_value'][bc_pores]
+        # Write values to self to to quantity, ic and t=0 array
+        quantity = self.settings['quantity']
+        self[quantity] = ic_vals
+
     def _get_f1_f2_f3(self):
         r"""
         Helper method: returns f1, f2, and f3 for _t_update_A and _t_update_b methods.
@@ -254,9 +263,11 @@ class TransientReactiveTransport(ReactiveTransport):
         """
         logger.info('―' * 80)
         logger.info('Running TransientTransport')
+        self._validate_settings()
         # If ICs are not defined, assume zero
         if not np.isfinite(self["pore.ic"]).all():
             self.set_IC(0)
+        self._overwrite_ICs_with_value_BCs()
         # Make sure _A is None to force _build_A, otherwise _A_steady might be wrong
         self._A = None
         # Save A matrix of the steady state problem (without BCs applied)
@@ -270,7 +281,6 @@ class TransientReactiveTransport(ReactiveTransport):
         self._b_t = self._b.copy()
         if t is None:
             t = self.settings['t_initial']
-        # Create S1 & S2 for 1st Picard's iteration
         self._update_iterative_props()
         self._run_transient(t=t)
 


Test patch:
diff --git a/tests/unit/algorithms/ReactiveTransportTest.py b/tests/unit/algorithms/ReactiveTransportTest.py
index 45fc311b1..4b70b92be 100644
--- a/tests/unit/algorithms/ReactiveTransportTest.py
+++ b/tests/unit/algorithms/ReactiveTransportTest.py
@@ -226,6 +226,18 @@ class ReactiveTransportTest:
         self.alg.reset(variable_props=True)
         assert not self.alg.settings["variable_props"]
 
+    def test_ensure_settings_are_valid(self):
+        alg = op.algorithms.ReactiveTransport(network=self.net,
+                                              phase=self.phase)
+        with pytest.raises(Exception, match=r".*quantity.*"):
+            alg.run()
+        alg.settings['quantity'] = 'pore.concentration'
+        with pytest.raises(Exception, match=r".*conductance.*"):
+            alg.run()
+        alg.settings['conductance'] = 'throat.conductance'
+        with pytest.raises(Exception):
+            alg.run()
+
     def teardown_class(self):
         ws = op.Workspace()
         ws.clear()
diff --git a/tests/unit/algorithms/TransientAdvectionDiffusionTest.py b/tests/unit/algorithms/TransientAdvectionDiffusionTest.py
index d46ab57e2..6c1bfc040 100644
--- a/tests/unit/algorithms/TransientAdvectionDiffusionTest.py
+++ b/tests/unit/algorithms/TransientAdvectionDiffusionTest.py
@@ -67,8 +67,8 @@ if __name__ == '__main__':
 
     t = TransientAdvectionDiffusionTest()
     t.setup_class()
+    self = t
     for item in t.__dir__():
         if item.startswith('test'):
             print('running test: '+item)
             t.__getattribute__(item)()
-    self = t
diff --git a/tests/unit/algorithms/TransientReactiveTransportTest.py b/tests/unit/algorithms/TransientReactiveTransportTest.py
index 846d6433f..19dba4223 100644
--- a/tests/unit/algorithms/TransientReactiveTransportTest.py
+++ b/tests/unit/algorithms/TransientReactiveTransportTest.py
@@ -37,7 +37,8 @@ class TransientImplicitReactiveTransportTest:
                        t_initial=0, t_final=1, t_step=0.1, t_tolerance=1e-7,
                        t_precision=10, rxn_tolerance=1e-6)
         self.alg.set_value_BC(pores=self.net.pores('back'), values=2)
-        self.alg.set_source(propname='pore.reaction', pores=self.net.pores('front'))
+        self.alg.set_source(propname='pore.reaction',
+                            pores=self.net.pores('front'))
         self.alg.set_IC(0)
 
     def test_transient_implicit_reactive_transport(self):
@@ -52,13 +53,13 @@ class TransientImplicitReactiveTransportTest:
     def test_transient_cranknicolson_reactive_transport(self):
         self.alg.setup(t_scheme='cranknicolson')
         self.alg.run()
-        x = [2, 0.94663043, 0.39438009,
-             2, 0.94663043, 0.39438009,
-             2, 0.94663043, 0.39438009]
+        x = [2., 0.97167537, 0.4209642,
+             2., 0.97167537, 0.4209642,
+             2., 0.97167537, 0.4209642]
         y = self.alg["pore.concentration"]
         nt.assert_allclose(y, x, rtol=1e-5)
 
-    def test_transient_reactive_transport_output(self):
+    def test_transient_reactive_transport_output_times(self):
         self.alg.setup(t_output=[0, 0.5, 0.7, 1])
         self.alg.run()
         times = ["pore.concentration@0",
@@ -109,13 +110,20 @@ class TransientImplicitReactiveTransportTest:
 
     def test_adding_sources_over_bc(self):
         with pytest.raises(Exception):
-            self.alg.set_source(propname='pore.reaction', pores=self.net.pores('left'))
+            self.alg.set_source(propname='pore.reaction',
+                                pores=self.net.pores('left'))
 
-    def test_set_IC_exception_if_quantity_not_specified(self):
+    def test_ensure_settings_are_valid(self):
         alg = op.algorithms.TransientReactiveTransport(network=self.net,
                                                        phase=self.phase)
+        with pytest.raises(Exception, match=r".*quantity.*"):
+            alg.run()
+        alg.settings['quantity'] = 'pore.concentration'
+        with pytest.raises(Exception, match=r".*conductance.*"):
+            alg.run()
+        alg.settings['conductance'] = 'throat.conductance'
         with pytest.raises(Exception):
-            alg.set_IC(0)
+            alg.run()
 
     def teardown_class(self):
         ws = op.Workspace()
@@ -126,8 +134,8 @@ if __name__ == '__main__':
 
     t = TransientImplicitReactiveTransportTest()
     t.setup_class()
+    self = t
     for item in t.__dir__():
         if item.startswith('test'):
             print('running test: '+item)
             t.__getattribute__(item)()
-    self = t