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.

翻译：数值模拟是科学研究的主流方法之一，通常由专业工程师执行。随着多智能体技术的发展，利用协作智能体复现人类行为在智能计算流体力学模拟中展现出巨大潜力。已有一些基于大语言模型的多智能体系统被提出，但在处理复杂几何结构时存在显著局限性。本文提出一种新型多智能体模拟框架SwarmFoam。该框架集成了多模态感知、智能纠错与检索增强生成等功能，旨在通过对图像与高级指令的双重解析实现更复杂的模拟。实验结果表明，SwarmFoam对不同模态的模拟输入具有良好的适应性：在25个测试案例中总体通过率达84%，其中自然语言与多模态输入案例的通过率分别为80%和86.7%。SwarmFoam所呈现的工作将进一步推动CFD领域智能体方法的发展。