Computational Fluid Dynamics (CFD) is critical for scientific advancement but is hindered by operational complexity and high expertise barriers. This paper introduces ChatCFD, a Large Language Model (LLM)-driven multi-agent system designed for end-to-end CFD automation using OpenFOAM. Powered by DeepSeek-R1/V3, ChatCFD integrates structured domain knowledge bases, a precise error locator, and iterative reflection to dramatically outperform existing methods. On 315 benchmark cases, ChatCFD achieves 82.1% execution success (vs. 6.2% for MetaOpenFOAM and 42.3% for Foam-Agent) and 68.12% physical fidelity - a novel metric assessing scientific meaningfulness beyond mere runnability. A dedicated Physics Interpreter attains 97.4% summary fidelity, bridging the gap between narrative fluency and the enforcement of tight physical constraints. Resource analysis confirms efficiency, averaging 192.1k tokens and $0.208 per case, significantly lower than baseline costs. Ablation studies identify the Error Locator and Solver Template DB as critical, with the latter's removal collapsing accuracy to 48%. The system exhibits robust flexibility, achieving 95.23% success in autonomous solver selection and 100% in turbulence modeling, while successfully reproducing complex literature cases (e.g., NACA0012, supersonic nozzle) with 60-80% success rates where baselines failed. Featuring a modular, MCP-compatible design, ChatCFD facilitates scalable, collaborative AI-driven CFD. Code is available at: https://github.com/ConMoo/ChatCFD

翻译：计算流体动力学（CFD）对科学进步至关重要，但其操作复杂性和高专业门槛构成了显著障碍。本文提出ChatCFD，一种基于大语言模型（LLM）驱动的多智能体系统，旨在利用OpenFOAM实现端到端的CFD自动化。该系统以DeepSeek-R1/V3为核心，集成了结构化领域知识库、精确错误定位器与迭代反思机制，性能显著超越现有方法。在315个基准案例上，ChatCFD实现了82.1%的执行成功率（对比MetaOpenFOAM的6.2%与Foam-Agent的42.3%）以及68.12%的物理保真度——后者是超越单纯可运行性、评估科学意义的新颖指标。专用的物理解释器实现了97.4%的摘要保真度，弥合了叙述流畅性与严格物理约束执行之间的差距。资源分析证实了其高效性，平均每案例消耗192.1k tokens和0.208美元，显著低于基线成本。消融实验表明错误定位器与求解器模板数据库至关重要，移除后者会使准确率骤降至48%。该系统展现出强大的灵活性，在自主求解器选择中达到95.23%的成功率，在湍流建模中达到100%，并成功复现了复杂文献案例（如NACA0012翼型、超音速喷管），在基线方法失败的场景中取得了60-80%的成功率。ChatCFD采用模块化且兼容MCP的设计，为可扩展、协作式的AI驱动CFD研究提供了支持。代码已开源：https://github.com/ConMoo/ChatCFD