remove redundant keyword arg

082e28a6 · joergbuchwald · 2139a465 · 082e28a6
Commit 082e28a6 authored 2 years ago by joergbuchwald
--- a/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb
+++ b/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb
@@ -75,7 +75,7 @@
    "respvars = [\"temperature\", \"pressure\"]\n",
    "for model in file:\n",
    "    resp[model] = {}\n",
-    "    f = vtuIO.VTUIO(file[model], nneighbors=100, dim=2)\n",
+    "    f = vtuIO.VTUIO(file[model], dim=2)\n",
    "    for var in respvars:\n",
    "        if \"M\" in model:\n",
    "            resp[model][var] = f.get_set_data(f\"{var}_interpolated\",pointsetarray=r)\n",

 %% Cell type:raw id: tags:

 author = "Jörg Buchwald"
 date = "2022-05-27T12:39:58+01:00"
 title = "Thermo-Osmosis in a one-dimensional column"
 weight = 70
 notebook = "Notebooks/thermo-osmosis.run-skip.ipynb"
 web_subsection = "thermo-hydro-mechanics"
 <!--eofm-->

 %% Cell type:markdown id: tags:

 ## Problem description

 The problem describes a one-dimensional column at $T$=300 K in sudden contact with a temperature reservoir at one side at $T_1$ = 350 K.

 Thermo-osmotic and filtration effects are described by contributions to the hydraulic flux $J^w$
 \begin{equation}
 J^w=-\rho_w \frac{\mathbf{k}}{\mu}\left(\nabla p-\rho_w \mathbf{g} \right)-\rho_w \mathbf{k}_{pT} \nabla T,
 \end{equation}
 and the conductive heat flux $I$
 \begin{equation}
 I=- \mathbf{\lambda}_s (1-\phi)+\mathbf{\lambda}_w \phi)- \mathbf{k}_{Tp} \nabla p,
 \end{equation}

 where $\mathbf{k}_{pT}$ is the phenomenological coefficient of thermo-osmosis and $\mathbf{k}_{Tp}$ the phenomenological coefficient of thermo-filtration.
 It can be shown that $\mathbf{k}_{Tp}=T*\mathbf{k}_{pT}$ (Zhou et al. 1998).

 %% Cell type:markdown id: tags:


 ## Get benchmark results

 %% Cell type:code id: tags:

 ``` python
 import os
 import vtuIO
 import numpy as np

 filename = "expected_Column_ts_68_t_7200000.000000.vtu"
 data_dir = os.environ.get('OGS_DATA_DIR', '../../Data')
 file = {}
 file["THM"] = f"{data_dir}/ThermoHydroMechanics/Linear/ThermoOsmosis/{filename}"
 file["TR"] = f"{data_dir}/ThermoRichardsFlow/ThermoOsmosis/{filename}"
 file["TRM"] = f"{data_dir}/ThermoRichardsMechanics/ThermoOsmosis/{filename}"
 x=np.array([i*0.1 for i in range(200)])
 r = np.array([[i,0.5,0.0] for i in x])
 resp = {}
 respvars = ["temperature", "pressure"]
 for model in file:
    resp[model] = {}
-    f = vtuIO.VTUIO(file[model], nneighbors=100, dim=2)
+    f = vtuIO.VTUIO(file[model], dim=2)
    for var in respvars:
        if "M" in model:
            resp[model][var] = f.get_set_data(f"{var}_interpolated",pointsetarray=r)
        else:
            resp[model][var] = f.get_set_data(f"{var}",pointsetarray=r)
 ```

 %% Output

    /home/buchwalj/.local/lib/python3.10/site-packages/vtuIO.py:147: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
      df["r_"+str(i)] = (df[x]-val[x]) * (df[x]-val[x]) + (df[y]-val[y]) * (df[y]-val[y])

 %% Cell type:markdown id: tags:

 ## Read-in the analytical solution

 An analytical solution was provided by Zhou et al. 1998 and can be obtained via [github](https://github.com/joergbuchwald/thermo-osmosis_analytical_solution).
 For this example we used $\mathbf{k}_{pT}=2.7e-10\, m^2/(s K)$ and a fully saturated material. More details on model parameters can be found in the corresponding project files.
 The Thermo-Richards (TR) model uses a correction to account for mechanical effects in the mass-balance equation. See Buchwald et al. 2021 for further details.

 %% Cell type:code id: tags:

 ``` python
 import zhou_solution_thermo_osmosis
 aTO = zhou_solution_thermo_osmosis.ANASOL(0,50,100)
 aNoTO = zhou_solution_thermo_osmosis.ANASOL(0,50,100)
 aNoTO.Sw = 0
 t=7.2e6
 ```

 %% Cell type:markdown id: tags:

 ## Plot temperature and pressure along the column

 %% Cell type:code id: tags:

 ``` python
 import matplotlib.pyplot as plt
 plt.rcParams['figure.figsize'] = (12, 10)
 plt.rcParams['font.size'] =  22
 marker = ['|', '+', 'x']

 for i, model in enumerate(resp):
    plt.plot(x,resp[model]["temperature"], marker[i], label=model)
 plt.plot(x,(aTO.T(x,t,10)+300), label="analytical solution")
 plt.plot(x,(aNoTO.T(x,t,10)+300), label="analytical solution no thermo-osmosis")
 plt.xlabel("$x$ / m")
 plt.xlim([0,20])
 plt.ylabel("$T$ / K")
 plt.legend()
 plt.title("temperature");
 ```

 %% Output



 %% Cell type:code id: tags:

 ``` python
 for i, model in enumerate(resp):
    plt.plot(x,resp[model]["pressure"], marker[i], label=model)
 plt.plot(x,(aTO.p(x,t,10)), label="analytical solution")
 plt.plot(x,(aNoTO.p(x,t,10)), label="analytical solution, no thermo-osmosis")
 plt.xlabel("$x$ / m")
 plt.ylabel("$p$ / Pa")
 plt.xlim([0,20])
 plt.legend()
 plt.title("pressure");
 ```

 %% Output



 %% Cell type:markdown id: tags:

 ## Diffference between analytical and the numerical solution:

 %% Cell type:code id: tags:

 ``` python
 for i, model in enumerate(resp):
    plt.plot(x,(resp[model]["temperature"]-(aTO.T(x,t,10)+300)), marker[i], label=model)
 plt.xlabel("$x$ / m")
 plt.xlim([0,20])
 plt.ylabel("$\Delta T$ / K")
 plt.legend()
 plt.title("temperature");
 ```

 %% Output



 %% Cell type:code id: tags:

 ``` python
 for i, model in enumerate(resp):
    plt.plot(x,resp[model]["pressure"]-aTO.p(x,t,200), marker[i], label=model)
 plt.xlabel("$x$ / m")
 plt.ylabel("$\Delta p$ / Pa")
 plt.xlim([0,20])
 plt.legend()
 plt.title("pressure");
 ```

 %% Output



 %% Cell type:markdown id: tags:

 The differences between the analytical solution and OGS is assumed to come from the neglectance of the advective heat-flux in the analytical solution.

 ## References

 [1] Zhou, Y., Rajapakse, R. K. N. D., & Graham, J. (1998). A coupled thermoporoelastic model with thermo-osmosis and thermal-filtration, International Journal of Solids and Structures, 35(34-35), 4659-4683.

 [2] Buchwald, J., Kaiser, S., Kolditz, O., & Nagel, T. (2021). Improved predictions of thermal fluid pressurization in hydro-thermal models based on consistent incorporation of thermo-mechanical effects in anisotropic porous media. International Journal of Heat and Mass Transfer, 172, 121127.