In this work, we introduce an open-source integrated CAD-CFD tool, Anvil, which combines FreeCAD for CAD modeling and OpenFOAM for CFD analysis, along with an AI-based optimization method (Bayesian optimization) and other sampling algorithms. Anvil serves as a scientific machine learning tool for shape optimization in three modes: data generation, CFD evaluation, and shape optimization. In data generation mode, it automatically runs CFD evaluations and generates data for training a surrogate model. In optimization mode, it searches for the optimal design under given requirements and optimization metrics. In CFD mode, a single CAD file can be evaluated with a single OpenFOAM run. To use Anvil, experimenters provide a JSON configuration file and a parametric CAD seed design. Anvil can be used to study solid-fluid dynamics for any subsonic flow conditions and has been demonstrated in various simulation and optimization use cases. The open-source code for the tool, installation process, artifacts (such as CAD seed designs and example STL models), experimentation results, and detailed documentation can be found at \url{https://github.com/symbench/Anvil}.

翻译：本研究介绍了一款开源集成CAD-CFD工具Anvil，其整合了用于CAD建模的FreeCAD与用于CFD分析的OpenFOAM，并融合了基于人工智能的优化方法（贝叶斯优化）及其他采样算法。Anvil作为面向形状优化的科学机器学习工具，提供三种工作模式：数据生成模式、CFD评估模式和形状优化模式。在数据生成模式下，该工具可自动执行CFD评估并生成用于训练代理模型的数据；在优化模式下，可根据给定需求与优化指标搜索最优设计方案；在CFD模式下，可通过单次OpenFOAM运行评估单个CAD文件。用户需提供JSON配置文件及参数化CAD种子设计即可使用Anvil。该工具适用于任何亚音速流动条件下的固-流体动力学研究，并已在多种仿真与优化应用场景中得到验证。工具的开源代码、安装流程、相关资源（如CAD种子设计与示例STL模型）、实验结果及详细文档可通过\url{https://github.com/symbench/Anvil}获取。