Foam-Agent: Towards Automated Intelligent CFD Workflows
This work addresses the barrier to entry for non-experts in engineering disciplines by lowering the CFD expertise threshold, though it is incremental as it builds on existing multi-agent and retrieval systems.
The paper tackles the problem of automating complex Computational Fluid Dynamics (CFD) simulation workflows, which typically require substantial domain expertise, by presenting Foam-Agent, a multi-agent framework that achieves an 83.6% success rate on 110 simulation tasks, significantly outperforming existing frameworks.
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