""" Henry ===== The classic 2D Henry problem demonstrates the development of a fresh-salt interface. """ # %% # We'll start with the usual imports import numpy as np import pandas as pd import xarray as xr import imod # %% # Discretization # -------------- # # We'll start off by creating a model discretization, since # this is a simple conceptual model. # The model is a 2D cross-section, hence nrow = 1. nrow = 1 ncol = 100 nlay = 50 dz = 1.0 dx = 1.0 dy = -dx top1D = xr.DataArray( np.arange(nlay * dz, 0.0, -dz), {"layer": np.arange(1, 1 + nlay)}, ("layer") ) bot = top1D - 1.0 # %% # Set up ibound, which sets where active cells are (ibound = 1.0) bnd = xr.DataArray( data=np.full((nlay, nrow, ncol), 1.0), coords={ "y": [0.5], "x": np.arange(0.5 * dx, dx * ncol, dx), "layer": np.arange(1, 1 + nlay), "dx": dx, "dy": dy, }, dims=("layer", "y", "x"), ) # Boundary Conditions # ------------------- # # We define constant head here, after generating the tops, or we'd end up with negative top values bnd[:, :, -1] = -1 # %% # Create WEL package # # First we scale the pumping rate with discretization qscaled = 0.03 * (dz * abs(dy)) # %% # Fresh water injection with well # Add the arguments as a list, so pandas doesn't complain about having to set # an index. weldata = pd.DataFrame() weldata["x"] = [0.5] weldata["y"] = [0.5] weldata["q"] = [qscaled] # %% # Build # ----- # # Finally, we build the model. m = imod.wq.SeawatModel("Henry") m["bas"] = imod.wq.BasicFlow(ibound=bnd, top=50.0, bottom=bot, starting_head=1.0) m["lpf"] = imod.wq.LayerPropertyFlow( k_horizontal=10.0, k_vertical=10.0, specific_storage=0.0 ) m["btn"] = imod.wq.BasicTransport( icbund=bnd, starting_concentration=35.0, porosity=0.35 ) m["adv"] = imod.wq.AdvectionTVD(courant=1.0) m["dsp"] = imod.wq.Dispersion(longitudinal=0.1, diffusion_coefficient=1.0e-9) m["vdf"] = imod.wq.VariableDensityFlow(density_concentration_slope=0.71) m["wel"] = imod.wq.Well( id_name="well", x=weldata["x"], y=weldata["y"], rate=weldata["q"] ) m["pcg"] = imod.wq.PreconditionedConjugateGradientSolver( max_iter=150, inner_iter=30, hclose=0.0001, rclose=1.0, relax=0.98, damp=1.0 ) m["gcg"] = imod.wq.GeneralizedConjugateGradientSolver( max_iter=150, inner_iter=30, cclose=1.0e-6, preconditioner="mic", lump_dispersion=True, ) m["oc"] = imod.wq.OutputControl(save_head_idf=True, save_concentration_idf=True) m.create_time_discretization( additional_times=pd.date_range("2000-01-01", "2001-01-01", freq="M") ) # %% # Now we write the model modeldir = imod.util.temporary_directory() m.write(modeldir, resultdir_is_workdir=True) # %% # Run # --- # # You can run the model using the comand prompt and the iMOD-WQ executable. # This is part of the iMOD v5 release, which can be downloaded here: # https://oss.deltares.nl/web/imod/download-imod5 . # This only works on Windows. # # To run your model, open up a command prompt # and run the following commands: # # .. code-block:: batch # # cd c:\path\to\modeldir # c:\path\to\imod\folder\iMOD-WQ_V5_3_SVN359_X64R.exe Henry.run # # Note that the version name of your executable might differ. # # %% # Visualise results # ----------------- # # After succesfully running the model, you can # plot results as follows: # # .. code:: python # # head = imod.idf.open(modeldir / "results/head/*.idf") # # fig, ax = plt.subplots() # head.plot(yincrease=False, ax=ax) # # conc = imod.idf.open(modeldir / "results/conc/*.idf") # # fig, ax = plt.subplots() # conc.plot(levels=range(0, 35, 5), yincrease=False, ax=ax) # # %% # %%