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}.

这项研究介绍了一种开源集成的CAD-CFD工具Anvil，它结合了FreeCAD进行CAD建模和OpenFOAM进行CFD分析，以及基于人工智能的优化方法（贝叶斯优化）和其他采样算法。Anvil在数据生成、CFD评估和形状优化这三个模式中作为科学机器学习工具，可用于研究任何亚声速流动条件下的固体-流体动力学，并在各种模拟和优化应用案例中得到了验证。

Anvil: 人工智能、采样技术和综合CAD-CFD工具的集成