用自然语言驱动大模型，自动完成流体模拟的敏感性分析与参数优化。
OptMetaOpenFOAM: Large Language Model Driven Chain of Thought for Sensitivity Analysis and Parameter Optimization based on CFD
- 通过大模型思维链解析用户自然语言指令，调用外部工具库自动执行任务。
- 200字符输入可触发超2000行代码的模拟、后处理、分析与优化全流程。
- 非专家也能高效完成复杂流体仿真，适合工业与科研场景快速迭代。
将自然语言接口与计算流体力学（CFD）工作流结合，为产业与研究带来变革性机遇。本文提出OptMetaOpenFOAM框架，通过大语言模型（LLM）驱动的思维链（COT）方法，实现MetaOpenFOAM与外部分析及优化工具库的联动。该框架利用自然语言输入自动化复杂CFD任务，显著提升非专家用户进行敏感性分析与参数优化的效率。测试包含11个不同CFD任务，涵盖一个基于OpenFOAM教程的基准模拟，涉及流体动力学、燃烧与传热。结果表明，OptMetaOpenFOAM能准确理解自然语言需求，并有效调用外部工具库与MetaOpenFOAM协同完成任务。在非OpenFOAM教程案例——氢气燃烧室中验证，仅需200字符的自然语言输入，即可触发跨越2000多行代码的模拟、后处理、分析与优化流程。这些发现凸显了LLM驱动思维链在连接外部工具以实现高级分析与优化方面的巨大潜力，使OptMetaOpenFOAM成为提升工业与科研应用中CFD模拟便捷性与效率的有效工具。代码已开源：https://github.com/Terry-cyx/MetaOpenFOAM。
原文摘要 · Abstract (English)
Merging natural language interfaces with computational fluid dynamics (CFD) workflows presents transformative opportunities for both industry and research. In this study, we introduce OptMetaOpenFOAM - a novel framework that bridges MetaOpenFOAM with external analysis and optimization tool libraries through a large language model (LLM)-driven chain-of-thought (COT) methodology. By automating complex CFD tasks via natural language inputs, the framework empowers non-expert users to perform sensitivity analyses and parameter optimizations with markedly improved efficiency. The test dataset comprises 11 distinct CFD analysis or optimization tasks, including a baseline simulation task derived from an OpenFOAM tutorial covering fluid dynamics, combustion, and heat transfer. Results confirm that OptMetaOpenFOAM can accurately interpret user requirements expressed in natural language and effectively invoke external tool libraries alongside MetaOpenFOAM to complete the tasks. Furthermore, validation on a non-OpenFOAM tutorial case - namely, a hydrogen combustion chamber - demonstrates that a mere 200-character natural language input can trigger a sequence of simulation, postprocessing, analysis, and optimization tasks spanning over 2,000 lines of code. These findings underscore the transformative potential of LLM-driven COT methodologies in linking external tool for advanced analysis and optimization, positioning OptMetaOpenFOAM as an effective tool that streamlines CFD simulations and enhances their convenience and efficiency for both industrial and research applications. Code is available at https://github.com/Terry-cyx/MetaOpenFOAM.
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