.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/imod-wq/Henry.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:here  to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_imod-wq_Henry.py: Henry ===== The classic 2D Henry problem demonstrates the development of a fresh-salt interface. .. GENERATED FROM PYTHON SOURCE LINES 10-11 We'll start with the usual imports .. GENERATED FROM PYTHON SOURCE LINES 11-17 .. code-block:: default import numpy as np import pandas as pd import xarray as xr import imod .. GENERATED FROM PYTHON SOURCE LINES 18-24 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. .. GENERATED FROM PYTHON SOURCE LINES 24-38 .. code-block:: default 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 .. GENERATED FROM PYTHON SOURCE LINES 39-40 Set up ibound, which sets where active cells are (ibound = 1.0) .. GENERATED FROM PYTHON SOURCE LINES 40-58 .. code-block:: default 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 .. GENERATED FROM PYTHON SOURCE LINES 59-62 Create WEL package First we scale the pumping rate with discretization .. GENERATED FROM PYTHON SOURCE LINES 62-64 .. code-block:: default qscaled = 0.03 * (dz * abs(dy)) .. GENERATED FROM PYTHON SOURCE LINES 65-68 Fresh water injection with well Add the arguments as a list, so pandas doesn't complain about having to set an index. .. GENERATED FROM PYTHON SOURCE LINES 68-73 .. code-block:: default weldata = pd.DataFrame() weldata["x"] = [0.5] weldata["y"] = [0.5] weldata["q"] = [qscaled] .. GENERATED FROM PYTHON SOURCE LINES 74-78 Build ----- Finally, we build the model. .. GENERATED FROM PYTHON SOURCE LINES 78-108 .. code-block:: default 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") ) .. GENERATED FROM PYTHON SOURCE LINES 109-110 Now we write the model .. GENERATED FROM PYTHON SOURCE LINES 110-114 .. code-block:: default modeldir = imod.util.temporary_directory() m.write(modeldir, resultdir_is_workdir=True) .. GENERATED FROM PYTHON SOURCE LINES 115-152 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) .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.354 seconds) .. _sphx_glr_download_examples_imod-wq_Henry.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:Download Python source code: Henry.py  .. container:: sphx-glr-download sphx-glr-download-jupyter :download:Download Jupyter notebook: Henry.ipynb  .. only:: html .. rst-class:: sphx-glr-signature Gallery generated by Sphinx-Gallery _