CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation

February 09, 2026

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📝 Original Info

Title: CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation
ArXiv ID: 2512.07917
Date: 2025-12-08
Authors: Zhehao Dong, Shanghai Du, Zhen Lu, Yue Yang

📝 Abstract

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 postprocessing. 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...

📄 Full Content

Computational fluid dynamics (CFD) is a cornerstone of modern aerospace engineering [1][2][3][4][5][6], serving as an indispensable tool for design and analysis. However, the utilization of CFD is hindered by its steep learning curve. Effectively conducting a simulation requires users to possess deep, interdisciplinary knowledge spanning fluid mechanics, numerical methods, and software-specific configurations. This high expertise barrier limits its accessibility, preventing many engineers and researchers from leveraging its full potential.

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