Fine-tuning a large language model for automating computational fluid dynamics simulations

作者：Zhehao Dong, Zhen Hua LU, Yang Yue · 发表于：Theoretical and Applied Mechanics Letters · 年份：2025 · DOI：10.1016/j.taml.2025.100594 · 被引用次数：28 · 研究领域：Meteorological Phenomena and Simulations、Scientific Computing and Data Management、Simulation Techniques and Applications

• We fine-tuned a domain-specific LLM, empowering a multi-agent system to automate CFD simulation from natural language input. • We developed NL2FOAM, a dataset containing 28716 cases designed for automated OpenFOAM simulation driven by natural language. • The system achieves an 82.6% first-attempt success rate on the benchmark with an average accuracy of 88.7%, demonstrating superior generalization and robustness. Configuring computational fluid dynamics (CFD) simulations typically demands extensive domain expertise, limiting broader access. Although large language models (LLMs) have advanced scientific computing, their use in automating CFD workflows is underdeveloped. We introduce a novel approach centered on domain-specific LLM adaptation. By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM, our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought (CoT) annotations enables direct translation from natural language descriptions to executable CFD setups. A multi-agent system orchestrates the process, autonomously verifying inputs, generating configurations, running simulations, and correcting errors. Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance, achieving 88.7% solution accuracy and 82.6% first-attempt success rate. This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct, DeepSeek-R1, and Llama3.3-70B-Instruct, while also requiring fewer correction iteration...