Foam-Agent: Towards Automated Intelligent CFD Workflows (2505.04997v1)

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

Overview of Foam-Agent: Towards Automated Intelligent CFD Workflows

The paper "Foam-Agent: Towards Automated Intelligent CFD Workflows" by Ling Yue et al. presents Foam-Agent, a multi-agent framework designed to streamline and automate workflows in Computational Fluid Dynamics (CFD) using OpenFOAM. In the field of scientific computing where CFD plays a crucial role in engineering disciplines, the framework addresses the profound complexity often faced by users that necessitates substantial expertise and manual configuration. Given the hierarchical and meticulous nature of CFD tasks, Foam-Agent aims to ease these constraints by providing a structured automation process from natural language inputs to simulations.

Key Innovations

Foam-Agent introduces several pivotal innovations aimed at tackling the inefficiencies in traditional CFD workflows:

1. Hierarchical Multi-Index Retrieval System: Foam-Agent employs a retrieval system that optimizes access to domain-specific knowledge segregated across different phases of the simulation process. This system enhances retrieval precision by leveraging specialized indices, thus ensuring context-specific guidance in simulation stages.
2. Dependency-Aware File Generation System: By enforcing consistency across configuration files, the system adheres to the logical dependencies of CFD simulations, which traditionally require familiarity with complex formatting and interdependencies.
3. Iterative Error Correction Mechanism: A standout feature is its ability to autonomously diagnose and correct simulation errors. This iterative mechanism draws from historical error patterns and solution trajectories to ensure accurate simulations without human intervention.

Experimental Results

Foam-Agent's efficacy was evaluated using a dataset comprising 110 simulation tasks. It achieved an 83.6% success rate with the Claude 3.5 Sonnet model, dramatically surpassing other frameworks such as MetaOpenFOAM and OpenFOAM-GPT variants, which managed 55.5% and 37.3% respectively. Ablation studies further highlight the critical contribution of each framework component, notably the error correction mechanism that boosted performance by 36.4%.

Practical Implications and Future Directions

Foam-Agent demonstrates promising potential in democratizing CFD access, reducing the expertise threshold required for effective simulation execution. Its application can foster greater interdisciplinary collaboration in fluid dynamics studies while maintaining the fidelity demanded by complex physical and engineering phenomena.

From a theoretical perspective, the integration of multi-agent systems in computational science opens pathways for enhancing automation in other scientific workflows. As the architecture matures, future iterations may focus on refining the consistency mechanisms and optimizing computational resource consumption during the iterative correction process, potentially incorporating more intelligent agentic interactions facilitated by advancements in AI and machine learning. Thus, Foam-Agent sets a benchmark in automated simulation tools, paving the way for sophisticated automated systems in scientific computing.