The purpose of this code and data is to enable reproduction (see Reproducing the manuscript) and facilitate extension of the computational results associated with Ref. [1] (see Citing This Work).

The container used in this work can be obtained using

docker pull rfd1/understanding-btcs:final

This requires Docker.

For users without previous Docker experience, we recommend the following steps

1. Install Docker Desktop.

2. Install Visual Studio Code.

3. Start the Docker Desktop application.

4. Open Visual Studio Code and install the Dev Containers extension.

5. With the Dev Containers extension installed, you will see the (green) new status bar at the bottom left

6. Click on the green status bar and then open this folder in a container.

A pdf version of the manual can be found at doc/manual.pdf.

To cite the manuscript, use Ref. [1]. To cite the software or data generated, use Ref. [2].

1. DeJaco, R. F.; Kearsley, A. J. Understanding Fast Adsorption in Single-Solute Breakthrough Curves, Communications in Nonlinear Science and Numerical Simulation, Volume 131, 2024, 107794, ISSN 1007-5704, doi: 10.1016/j.cnsns.2023.107794.

2. De Jaco, R. F. Sofware and Data Associated with "DeJaco, R. F.; Kearsley, A. J. Understanding Fast Adsorption in Breakthrough-curve Measurements." National Institute of Standards and Technology, 2023, doi: 10.18434/mds2-3103.

Performing the calculations via

bash calc_figureS1.sh

generates the output files

• spatial.csv
• temporal1.csv
• temporal2.csv
• temporal3.csv.

Plotting the results via

python3 plot_figureS1.py

generates the figure out/figureS1.png.

Performing the calculations via

bash calc_figureS2.sh

generates the output files

• spatial-refinement-kappa=-0.8.csv
• spatial-refinement-kappa=-0.4.csv
• spatial-refinement-kappa=0.csv
• spatial-refinement-kappa=1.csv
• spatial-refinement-kappa=8.csv
• spatial-refinement-kappa=64.csv
• spatial-refinement-kappa=256.csv
• temporal-refinement-kappa=-0.8.csv
• temporal-refinement-kappa=-0.4.csv
• temporal-refinement-kappa=0.csv
• temporal-refinement-kappa=1.csv
• temporal-refinement-kappa=8.csv
• temporal-refinement-kappa=64.csv
• temporal-refinement-kappa=256.csv

Plotting the results via

python3 plot_figureS2.py

uses these output files to generate the figure out/figureS2.png.

Performing the calculations via

bash calc_figure3_figure4_figureS3.sh

generates the following output files:

• rarefaction-0.02.dat
• rarefaction-0.04.dat
• rarefaction-0.08.dat
• rarefaction-0.16.dat
• rarefaction-0.32.dat
• rarefaction-0.64.dat
• shock-0.02.dat
• shock-0.04.dat
• shock-0.08.dat
• shock-0.16.dat
• shock-0.32.dat
• shock-0.64.dat
• rarefaction--0.1-0.02.dat
• rarefaction--0.2-0.02.dat
• rarefaction--0.4-0.02.dat
• rarefaction--0.8-0.02.dat
• rarefaction--0.1-0.04.dat
• rarefaction--0.2-0.04.dat
• rarefaction--0.4-0.04.dat
• rarefaction--0.8-0.04.dat
• rarefaction--0.1-0.08.dat
• rarefaction--0.2-0.08.dat
• rarefaction--0.4-0.08.dat
• rarefaction--0.8-0.08.dat
• rarefaction--0.1-0.16.dat
• rarefaction--0.2-0.16.dat
• rarefaction--0.4-0.16.dat
• rarefaction--0.8-0.16.dat
• rarefaction--0.1-0.32.dat
• rarefaction--0.2-0.32.dat
• rarefaction--0.4-0.32.dat
• rarefaction--0.8-0.32.dat
• rarefaction--0.1-0.64.dat
• rarefaction--0.2-0.64.dat
• rarefaction--0.4-0.64.dat
• rarefaction--0.8-0.64.dat

These figures can be generated by

python3 plot_figure3_figureS3.py && python3 plot_figure4.py

and their output .png files found in the out/ folder as before.

Performing the calculations via

bash calc_figure5.sh

generates the output files that can be found in folders

• movie-1.0
• movie-6.0
• movie-36.0

The figure is generated via

python3 plot_figure5.py