Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool
This tool addresses the need for streamlined shape optimization in engineering and scientific simulations, though it is incremental as it combines existing components like FreeCAD, OpenFOAM, and Bayesian optimization.
The authors tackled the problem of shape optimization in computational fluid dynamics by developing Anvil, an open-source tool that integrates CAD modeling, CFD analysis, and AI-based optimization, resulting in a system that automates data generation, CFD evaluation, and optimization for subsonic flow conditions.
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}.