Simulations, data, and scripts for Hydrodynamics of bubble flow through a porous medium with applications to packed bed reactors

<p>Five sets of simulation files are zipped into individual archives and named (as "We_dp_..._selected_files.zip") for each global Weber number, <em>We<sub>dp</sub></em>, described in the manuscript "Hydrodynamics of bubble flow through a porous medium with applications to packed bed reactors" by Nagrani, Marconnet & Christov (2023).</p>

<p>Each zip archive contains ANSYS Fluent 2022R1 case files and selected output data files, which allows the user to identify the simulation that this data corresponds to in the manuscript. Post-processing was done using ANSYS Fluent to generate data, stored in an Excel spreadsheet, and results were plotted using MATLAB R2019a. </p>

<p>The archive "Pre_processing_files_and_post_processing_files.zip" contains the data spreadsheet, the MATLAB scripts, as well as Blender 2.92.0 simulation files, and ANSYS SpaceClaim geometry files (.scdoc and .fmd). Blender was used to generate the packed-bed geometry, which was cleaned up and made ready using SpaceClaim for the Fluent meshing and simulations. The mesh used is also provided in the archive as an .msh file.</p>

<p>Inferences based on these simulations and data, and a discussion of their meaning/implications, can be found in the manuscript (available as a preprint at <a href="https://arxiv.org/abs/2202.11704" rel="nofollow noopener noreferrer" target="_blank">https://arxiv.org/abs/2308.08075</a>).</p>

五组仿真文件被打包为独立归档文件，针对每一个全局韦伯数（Weber number），均以`We_dp_..._selected_files.zip`格式命名。相关内容详见Nagrani、Marconnet与Christov于2023年发表的学术论文《多孔介质内气泡流动的水动力学特性及其在填充床反应器中的应用》。

每个归档文件均包含ANSYS Fluent 2022R1算例文件与选定的输出数据文件，便于使用者快速定位论文中对应的仿真工况。本数据集的后处理通过ANSYS Fluent完成，生成的数据存储于Excel电子表格中，结果绘图则采用MATLAB R2019a实现。

归档文件`Pre_processing_files_and_post_processing_files.zip`内含数据电子表格、MATLAB脚本、Blender 2.92.0仿真文件，以及ANSYS SpaceClaim几何文件（格式为.scdoc与.fmd）。其中，填充床几何模型由Blender生成，随后通过ANSYS SpaceClaim进行清理与预处理，以适配ANSYS Fluent的网格划分与仿真流程。本次仿真所用的网格文件亦以.msh格式存储于该归档内。

基于本数据集仿真与数据得出的推论，以及对其内涵与应用价值的讨论，均可在该论文中找到。该论文的预印本可通过https://arxiv.org/abs/2308.08075获取。