ChannelFlow-Tools：可配置的 CFD 数据集流水线 ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows

精读摘要

计算流体力学（CFD）越来越多使用数据驱动代理模型，可靠性取决于训练数据质量，但现有模型常训练在固定、预生成的数据集上。系统性的代理模型研究需要可控的数据生成——数据集能按研究需求重新生成、适配或扩展。ChannelFlow-Tools 是一个开源、配置驱动的流水线，用于生成三维阻塞通道流的机器学习就绪数据集，为 CFD 代理模型研究补上了数据工程这块短板。 Data-driven surrogate models in CFD depend on training data quality, but models are typically trained on fixed, pre-generated datasets. Systematic surrogate studies require controlled data generation where datasets can be regenerated, adapted, or extended. ChannelFlow-Tools is an open-source, configuration-driven pipeline for generating ML-ready datasets of 3D obstructed channel flows.