SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Models

2026-01-12

AI Summary

This work proposes SwarmFoam, the first large language model–based multi-agent framework that integrates multimodal perception, retrieval-augmented generation (RAG), and an intelligent error-correction mechanism to address the challenges of automating high-fidelity computational fluid dynamics (CFD) simulations in complex geometries. Traditional multi-agent systems struggle to effectively fuse multimodal inputs and achieve robust automation in such settings. SwarmFoam overcomes these limitations by jointly interpreting visual and natural language instructions to autonomously drive OpenFOAM simulations. Evaluated on 25 test cases, the system achieves an overall success rate of 84%, with 80% accuracy for natural language inputs and 86.7% for multimodal inputs, demonstrating significantly enhanced adaptability and automation capabilities for handling intricate geometries and diverse multimodal commands.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Numerical simulation is one of the mainstream methods in scientific research, typically performed by professional engineers. With the advancement of multi-agent technology, using collaborating agents to replicate human behavior shows immense potential for intelligent Computational Fluid Dynamics (CFD) simulations. Some muti-agent systems based on Large Language Models have been proposed. However, they exhibit significant limitations when dealing with complex geometries. This paper introduces a new multi-agent simulation framework, SwarmFoam. SwarmFoam integrates functionalities such as Multi-modal perception, Intelligent error correction, and Retrieval-Augmented Generation, aiming to achieve more complex simulations through dual parsing of images and high-level instructions. Experimental results demonstrate that SwarmFoam has good adaptability to simulation inputs from different modalities. The overall pass rate for 25 test cases was 84%, with natural language and multi-modal input cases achieving pass rates of 80% and 86.7%, respectively. The work presented by SwarmFoam will further promote the development of intelligent agent methods for CFD.

Problem

• Computational Fluid Dynamics
• multi-agent system
• complex geometries
• multimodal input
• Large Language Models

Innovation

• Multi-agent system
• Large Language Models
• Computational Fluid Dynamics
• Multi-modal perception
• Retrieval-Augmented Generation