Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913415652442112 |
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| author | Vardhan, Harsh Timalsina, Umesh Sandborn, Michael Hyde, David Volgyesi, Peter Sztipanovits, Janos |
| author_facet | Vardhan, Harsh Timalsina, Umesh Sandborn, Michael Hyde, David Volgyesi, Peter Sztipanovits, Janos |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02519 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool Vardhan, Harsh Timalsina, Umesh Sandborn, Michael Hyde, David Volgyesi, Peter Sztipanovits, Janos Computational Engineering, Finance, and Science Machine Learning Numerical Analysis 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}. |
| title | Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool |
| topic | Computational Engineering, Finance, and Science Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2407.02519 |