Foam-Agent: Towards Automated Intelligent CFD Workflows

• URL: http://arxiv.org/abs/2505.04997v1
• Date: Thu, 08 May 2025 07:05:51 GMT
• Title: Foam-Agent: Towards Automated Intelligent CFD Workflows
• Authors: Ling Yue, Nithin Somasekharan, Yadi Cao, Shaowu Pan,
• Abstract summary: We present Foam-Agent, a multi-agent framework that automates complex OpenFOAM-based CFD simulation from natural language inputs.<n>Our innovation includes (1) a hierarchical multi-index retrieval system with specialized indices for different simulation aspects, (2) a dependency-aware file generation system that provides consistency management across configuration files, and (3) an iterative error correction mechanism that diagnoses and resolves simulation failures without human intervention.
• Score: 2.303486126296845
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computational Fluid Dynamics (CFD) is an essential simulation tool in various engineering disciplines, but it often requires substantial domain expertise and manual configuration, creating barriers to entry. We present Foam-Agent, a multi-agent framework that automates complex OpenFOAM-based CFD simulation workflows from natural language inputs. Our innovation includes (1) a hierarchical multi-index retrieval system with specialized indices for different simulation aspects, (2) a dependency-aware file generation system that provides consistency management across configuration files, and (3) an iterative error correction mechanism that diagnoses and resolves simulation failures without human intervention. Through comprehensive evaluation on the dataset of 110 simulation tasks, Foam-Agent achieves an 83.6% success rate with Claude 3.5 Sonnet, significantly outperforming existing frameworks (55.5% for MetaOpenFOAM and 37.3% for OpenFOAM-GPT). Ablation studies demonstrate the critical contribution of each system component, with the specialized error correction mechanism providing a 36.4% performance improvement. Foam-Agent substantially lowers the CFD expertise threshold while maintaining modeling accuracy, demonstrating the potential of specialized multi-agent systems to democratize access to complex scientific simulation tools. The code is public at https://github.com/csml-rpi/Foam-Agent

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