Problem
Research questions and friction points this paper is trying to address.
turbomachinery aerodynamic design
autonomous design
multi-stage optimization
end-to-end design
computational fluid dynamics
TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design
🤖 AI Summary
This study addresses the fragmented nature of conventional turbomachinery aerodynamic design processes, which hinder end-to-end autonomous optimization. To overcome this limitation, the authors propose a large language model (LLM)-driven multi-agent collaborative framework that, for the first time, employs an LLM as the central task planner to tightly integrate geometry generation, rapid performance prediction, multi-objective optimization, and high-fidelity CFD validation into a fully autonomous closed-loop pipeline—from natural language specifications to final design. Demonstrated on a transonic single-rotor compressor, the framework achieves R² values exceeding 0.91 and normalized RMSE below 8% for key performance metrics. Compared to the baseline design, it yields a 1.61% improvement in efficiency and a 3.02% increase in total pressure ratio, completing the entire workflow in approximately 30 minutes.
📝 Abstract
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 (R2) 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
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-driven multi-agent system
autonomous aerodynamic design
turbomachinery optimization
end-to-end design automation
physics-informed AI
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