# FLOW: Richards: 1D: Forward Simulation¶

The example shows simulation of Richards equation in 1D with a heterogeneous hydraulic conductivity function.

The haverkamp model is used with the same parameters as Celia1990 the boundary and initial conditions are also the same. The simulation domain is 40cm deep and is run for an hour with an exponentially increasing time step that has a maximum of one minute. The general setup of the experiment is an infiltration front that advances downward through the model over time.

Figure (a) shows the heterogeneous saturated hydraulic conductivity parameter and the location of the data collection, which happens every minute from 30 seconds into the simulation. Note that the simulation mesh and the data locations are not aligned, and linear interpolation is used to collect the data. The points are sampled in pressure head and then transformed to saturation using the haverkamp model for the water retention curve.

Figure (b) shows the data collected from the simulation. No noise is added to the data at this time. The various data locations register the infiltration event through increasing saturation as the front moves past the receiver. Notice that the slope of the curves are not equal as the hydraulic conductivity function is heterogeneous.

Figure (c) shows the saturation field over the entire experiment. Here you can see that the timestep is not constant over time (5 seconds at the start of the simulation, 60 seconds at the end). You can also see the effect of the highly conductive layer in the model between 20 and 25 cm depth. The water drains straight through the conductive unit and piles up on the other side - advancing the fluid front faster than the other layers.

Rowan Cockett - 21/12/2016

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 import matplotlib import matplotlib.pyplot as plt import numpy as np from SimPEG import Mesh, Maps from SimPEG.FLOW import Richards def run(plotIt=True): """ FLOW: Richards: 1D: Forward Simulation ====================================== The example shows simulation of Richards equation in 1D with a heterogeneous hydraulic conductivity function. The haverkamp model is used with the same parameters as Celia1990_ the boundary and initial conditions are also the same. The simulation domain is 40cm deep and is run for an hour with an exponentially increasing time step that has a maximum of one minute. The general setup of the experiment is an infiltration front that advances downward through the model over time. Figure (a) shows the heterogeneous saturated hydraulic conductivity parameter and the location of the data collection, which happens every minute from 30 seconds into the simulation. Note that the simulation mesh and the data locations are not aligned, and linear interpolation is used to collect the data. The points are sampled in pressure head and then transformed to saturation using the haverkamp model for the water retention curve. Figure (b) shows the data collected from the simulation. No noise is added to the data at this time. The various data locations register the infiltration event through increasing saturation as the front moves past the receiver. Notice that the slope of the curves are not equal as the hydraulic conductivity function is heterogeneous. Figure (c) shows the saturation field over the entire experiment. Here you can see that the timestep is not constant over time (5 seconds at the start of the simulation, 60 seconds at the end). You can also see the effect of the highly conductive layer in the model between 20 and 25 cm depth. The water drains straight through the conductive unit and piles up on the other side - advancing the fluid front faster than the other layers. Rowan Cockett - 21/12/2016 .. _Celia1990: http://www.webpages.uidaho.edu/ch/papers/Celia.pdf """ M = Mesh.TensorMesh([np.ones(40)], x0='N') M.setCellGradBC('dirichlet') # We will use the haverkamp empirical model with parameters from Celia1990 k_fun, theta_fun = Richards.Empirical.haverkamp( M, A=1.1750e+06, gamma=4.74, alpha=1.6110e+06, theta_s=0.287, theta_r=0.075, beta=3.96 ) # Here we are making saturated hydraulic conductivity # an exponential mapping to the model (defined below) k_fun.KsMap = Maps.ExpMap(nP=M.nC) # Setup the boundary and initial conditions bc = np.array([-61.5, -20.7]) h = np.zeros(M.nC) + bc[0] prob = Richards.RichardsProblem( M, hydraulic_conductivity=k_fun, water_retention=theta_fun, boundary_conditions=bc, initial_conditions=h, do_newton=False, method='mixed', debug=False ) prob.timeSteps = [(5, 25, 1.1), (60, 40)] # Create the survey locs = -np.arange(2, 38, 4.) times = np.arange(30, prob.timeMesh.vectorCCx[-1], 60) rxSat = Richards.SaturationRx(locs, times) survey = Richards.RichardsSurvey([rxSat]) survey.pair(prob) # Create a simple model for Ks Ks = 1e-3 mtrue = np.ones(M.nC)*np.log(Ks) mtrue[15:20] = np.log(5e-2) mtrue[20:35] = np.log(3e-3) mtrue[35:40] = np.log(1e-2) # Create some synthetic data and fields Hs = prob.fields(mtrue) data = survey.makeSyntheticData(mtrue, std=0, f=Hs, force=True) if plotIt: plt.figure(figsize=(14, 9)) plt.subplot(221) plt.plot(np.log10(np.exp(mtrue)), M.gridCC) plt.title('(a) True model and data locations') plt.ylabel('Depth, cm') plt.xlabel('Hydraulic conductivity, $log_{10}(K_s)$') plt.plot([-3.25]*len(locs), locs, 'ro') plt.legend(('True model', 'Data locations')) plt.subplot(222) plt.plot(times/60, data.reshape((-1, len(locs)))) plt.title('(b) True data over time at all depths') plt.xlabel('Time, minutes') plt.ylabel('Saturation') ax = plt.subplot(212) mesh2d = Mesh.TensorMesh([prob.timeMesh.hx/60, prob.mesh.hx], '0N') sats = [theta_fun(_) for _ in Hs] clr = mesh2d.plotImage(np.c_[sats][1:, :], ax=ax) cmap0 = matplotlib.cm.RdYlBu_r clr[0].set_cmap(cmap0) c = plt.colorbar(clr[0]) c.set_label('Saturation $\\theta$') plt.xlabel('Time, minutes') plt.ylabel('Depth, cm') plt.title('(c) Saturation over time') plt.tight_layout() if __name__ == '__main__': run() plt.show()