BioBrAIn: 700L bioreactor - CFD-derived data

This case is based on the studies from Spann et al. and Tajsoleiman et al. on the formation of pH gradients in a <strong>700 L </strong>batch fermentation of <em>Streptococcus thermophilus</em>. These studies employed a single-phase CFD simulation to predict the evolution of pH gradients experimentally observed, and then assessed their effect on growth when coupled to a biokinetic model. Later, Nadal-Rey et al. utilized this reactor geometry to propose a workflow for developing a dynamic compartment model based on a library of staggered simulations, aiming to obtain a close-to-real-time representation of the hydrodynamics and respective gradients of substrate and oxygen.<br>Here, we constrained ourselves to solely solve for the flow fields based on this bioreactor geometry for different combinations of operating conditions. The combinations of operating conditions were sampled via a Latin-Hypercube-Sampling (LHS) approach, which stratifies each factor and selects one value per stratum to achieve near-uniform coverage with far fewer samples than simple random sampling. Hence, two distinct sets are shared: 1) With fixed impeller configuration, and 2) variable impeller configuration. <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>本案例基于Spann等人与Tajsoleiman等人针对**700升（L）**嗜热链球菌（Streptococcus thermophilus）分批发酵过程中pH梯度形成的相关研究。上述两项工作采用单相计算流体动力学（Computational Fluid Dynamics, CFD）模拟，对实验观测到的pH梯度演化过程进行预测，并将模拟结果与生物动力学模型耦合后，评估了pH梯度对菌体生长的影响。后续，Nadal-Rey等人以此反应器几何构型为基础，提出了一种基于交错模拟库的动态分区模型开发流程，旨在实现流体动力学场、底物与氧浓度梯度的准实时表征。 本次研究仅针对该生物反应器的几何构型，针对不同操作条件组合求解流场。操作条件组合采用拉丁超立方抽样（Latin-Hypercube-Sampling, LHS）方法生成：该方法对每个因子进行分层抽样，从每一层中选取一个取值，相较于简单随机抽样，仅需更少的样本即可实现近乎均匀的覆盖范围。据此，本次共享了两组独立数据集：1）搅拌桨配置固定的数据集；2）搅拌桨配置可变的数据集。 关于网格划分流程、所采用的模拟模型及其参数化设置的更多细节，可参见README.md文件。 本数据集隶属于**BioBrAIn**数据集集合——《生物反应器计算流体动力学数据库》。