BioBrAIn: Industrial Bioreactor with Cooling Coil - CFD-derived data

The dataset comprises 760 CFD simulations generated from the Latin hypercube sampling (LHS) design-of-experiments. The motivation for generating this dataset was to train supervised AI\ML models to predict spatial concentration gradients in an industrial reactor under varying operating conditions. We split the data into a development set (560 simulations) for model selection and hyperparameter tuning, and an unseen test set (200 simulations) for final evaluation.<br>For transparency and reproducibility, the complete case lists - including all sampled input parameters for each simulation - are provided in the companion files <em>development_set_cases.csv </em>and <em>test_set_cases.csv</em>. Each row corresponds to a unique simulation (case ID) and can be used to reproduce or subset the experiments reported in this work.<br>Further details about the meshing workflow, models implemented, and their parameterization can be found in the README.md file. <br>This dataset is part of the <strong>BioBrAIn </strong>collection -<em> A CFD database of bioreactors. </em>

本数据集共计包含760组基于拉丁超立方采样（Latin Hypercube Sampling, LHS）实验设计生成的计算流体动力学（Computational Fluid Dynamics, CFD）仿真结果。构建本数据集的核心目标，是训练监督式人工智能/机器学习模型，以预测不同运行工况下工业反应器内的空间浓度梯度分布。我们将全部数据划分为开发集（560组仿真结果）与未知测试集（200组仿真结果）：前者用于模型选型与超参数调优，后者用于最终性能评估。
为保障研究的可复现性与透明度，完整案例列表（涵盖每组仿真的全部采样输入参数）已收录于配套文件<em>development_set_cases.csv</em>与<em>test_set_cases.csv</em>中。列表内每一行对应一组唯一的仿真（附带案例ID），可用于复现本文所述实验或对其进行子集划分。
有关网格划分流程、所采用模型及其参数化设置的更多细节，可查阅README.md文件。
本数据集隶属于<strong>BioBrAIn</strong>数据库集——<em>生物反应器计算流体动力学数据库</em>。