用大模型驱动多智能体，实现叶轮气动设计全流程自动化。
TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design
- 大模型统筹任务，各智能体分工完成设计生成、预测、优化与验证。
- 性能指标与仿真结果高度一致，效率提升1.61%，压比增3.02%。
- 从自然语言需求到设计完成仅需30分钟，适合工程研发快速迭代。
叶轮机械气动设计是一个涉及几何生成、性能预测、优化和高保真物理验证的复杂多阶段耦合过程。现有智能设计方法通常局限于单一阶段或依赖松散耦合流程，难以实现端到端自主设计。为此，本文提出TurboAgent，一个基于大语言模型（LLM）的自主多智能体框架，用于叶轮机械气动设计与优化。LLM作为核心任务规划与协调单元，专用智能体分别负责生成式设计、快速性能预测、多目标优化及基于物理的验证。该框架将传统试错设计转变为数据驱动的协同流程，保留高保真模拟用于最终验证。以跨音速单级压气机为例进行验证，生成设计与目标性能、CFD仿真结果高度一致，质量流量、总压比和等熵效率的决定系数均超过0.91，归一化均方根误差低于8%。优化智能体进一步提升等熵效率1.61%、总压比3.02%。在并行计算下，完整流程可在约30分钟内完成。结果表明，TurboAgent实现了从自然语言需求到最终设计生成的自主闭环设计，为叶轮机械气动设计提供高效可扩展的新范式。
原文摘要 · Abstract (English)
The aerodynamic design of turbomachinery is a complex and tightly coupled multi-stage process involving geometry generation, performance prediction, optimization, and high-fidelity physical validation. Existing intelligent design approaches typically focus on individual stages or rely on loosely coupled pipelines, making fully autonomous end-to-end design challenging. To address this issue, this study proposes TurboAgent, a large language model (LLM)-driven autonomous multi-agent framework for turbomachinery aerodynamic design and optimization. The LLM serves as the core for task planning and coordination, while specialized agents handle generative design, rapid performance prediction, multi-objective optimization, and physics-based validation. The framework transforms traditional trial-and-error design into a data-driven collaborative workflow, with high-fidelity simulations retained for final verification. A transonic single-rotor compressor is used for validation. The results show strong agreement between target performance, generated designs, and CFD simulations. The coefficients of determination for mass flow rate, total pressure ratio, and isentropic efficiency all exceed 0.91, with normalized RMSE values below 8%. The optimization agent further improves isentropic efficiency by 1.61% and total pressure ratio by 3.02%. The complete workflow can be executed within approximately 30 minutes under parallel computing. These results demonstrate that TurboAgent enables an autonomous closed-loop design process from natural language requirements to final design generation, providing an efficient and scalable paradigm for turbomachinery aerodynamic design.
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