Data for: Machine-Learning Based Error Prediction Approach for Coarse-Grid Computational Fluid Dynamics (CG-CFD)

Lid-driven cavity flow variables distribution in a cubic domain. Reynolds numbers range from 6000 :12000. Various grids and aspect ratios. These data are the inputs and the outputs for a regression model that predicts the local grid-induced error given the coarse-grid local fluid flow features.本数据集包含立方域内顶盖驱动方腔流（Lid-driven cavity flow）的变量分布数据，对应的雷诺数（Reynolds number）区间为6000至12000，且涵盖多种网格构型与长宽比配置。该数据集可作为回归模型的输入与输出样本，此模型能够基于粗网格局部流体流动特征，预测由网格诱导产生的局部误差。