Foam-Agent: Towards Automated Intelligent CFD Workflows

TL;DR

Foam-Agent is a multi-agent framework utilizing large language models to automate the entire CFD workflow, significantly reducing the need for expert intervention and streamlining complex fluid simulations.

Contribution

The paper introduces Foam-Agent, a novel multi-agent system that automates CFD workflows using LLMs, integrating retrieval-augmented generation and dependency-aware scheduling.

Findings

Achieved an 88.2% success rate on 110 CFD tasks.

Automated end-to-end CFD workflow from natural language prompts.

Reduced expertise barriers in complex fluid simulations.

Abstract

Computational fluid dynamics (CFD) has been the main workhorse of computational physics. Yet its steep learning curve and fragmented, multi-stage workflow create significant barriers. To address these challenges, we present Foam-Agent, a multi-agent framework leveraging large language models (LLMs) to automate the end-to-end CFD workflow from a single natural language prompt. Foam-Agent orchestrates the comprehensive simulation workflow from mesh generation and high-performance computing job scripting to post-processing visualization. The system integrates retrieval-augmented generation with dependency-aware scheduling to synthesize high-fidelity simulation configurations. Furthermore, Foam-Agent adopts the Model Context Protocol to expose its core functions as discrete, callable tools. This allows for flexible integration and use by any other agentic systems. Evaluated on 110 simulation tasks, Foam-Agent achieved a state-of-the-art execution success rate of 88.2% without expert intervention. These results demonstrate how specialized multi-agent systems can effectively reduce expertise barriers and streamline complex fluid simulations.