Automating Computational Fluid Dynamics with LLM-based Multi-Agent Systems

Abstract Computational Fluid Dynamics (CFD) accelerates scientific discovery in physical engineering, 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 to automate the end-to-end CFD workflow from a single natural language prompt. Foam-Agent orchestrates the comprehensive simulation lifecycle, ranging from mesh generation and high-performance computing script formulation 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 success rate of 88.2% without expert intervention. These results demonstrate how specialized multi-agent systems can effectively reduce expertise barriers and streamline complex scientific computing.