CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation
This work addresses the barrier of expertise in CFD simulation setup for engineers and researchers, though it appears incremental as it builds on existing LLM automation with domain-specific adaptations.
The authors tackled the challenge of automating computational fluid dynamics (CFD) simulations for non-specialists by introducing CFD-copilot, a domain-adapted large language model framework that translates natural language into executable setups and integrates with post-processing tools, resulting in enhanced reliability and efficiency in benchmarks like the NACA 0012 and 30P-30N airfoils.
Configuring computational fluid dynamics (CFD) simulations requires significant expertise in physics modeling and numerical methods, posing a barrier to non-specialists. Although automating scientific tasks with large language models (LLMs) has attracted attention, applying them to the complete, end-to-end CFD workflow remains a challenge due to its stringent domain-specific requirements. We introduce CFD-copilot, a domain-specialized LLM framework designed to facilitate natural language-driven CFD simulation from setup to post-processing. The framework employs a fine-tuned LLM to directly translate user descriptions into executable CFD setups. A multi-agent system integrates the LLM with simulation execution, automatic error correction, and result analysis. For post-processing, the framework utilizes the model context protocol (MCP), an open standard that decouples LLM reasoning from external tool execution. This modular design allows the LLM to interact with numerous specialized post-processing functions through a unified and scalable interface, improving the automation of data extraction and analysis. The framework was evaluated on benchmarks including the NACA~0012 airfoil and the three-element 30P-30N airfoil. The results indicate that domain-specific adaptation and the incorporation of the MCP jointly enhance the reliability and efficiency of LLM-driven engineering workflows.