Computational Fluid Dynamics (CFD) is essential for scientific and engineering advancements but is limited by operational complexity and the need for extensive expertise. This paper presents ChatCFD, a large language model-driven pipeline that automates CFD workflows within the OpenFOAM framework. It enables users to configure and execute complex simulations from natural language prompts or published literature with minimal expertise. The innovation is its structured approach to database construction, configuration validation, and error reflection, integrating CFD and OpenFOAM knowledge with general language models to improve accuracy and adaptability. Validation shows ChatCFD can autonomously reproduce published CFD results, handling complex, unseen configurations beyond basic examples, a task challenging for general language models.
View on arXiv@article{fan2025_2506.02019,
title={ ChatCFD: an End-to-End CFD Agent with Domain-specific Structured Thinking },
author={ E Fan and Weizong Wang and Tianhan Zhang },
journal={arXiv preprint arXiv:2506.02019},
year={ 2025 }
}