基于翼型几何可行性约束的旋翼气动外形优化方法和系统
By combining shape transformation and singular value decomposition methods with low-order aerodynamic models and neural networks, and introducing Euclidean distance constraints in modal space, the problem of insufficient geometric feasibility in rotor optimization design is solved, achieving efficient and automated three-dimensional collaborative optimization and generating manufacturable rotor aerodynamic shapes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG UNIV
- Filing Date
- 2026-05-11
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies lack geometric feasibility constraints in rotor optimization design, making it difficult to implement automated design processes, high-precision CFD optimization calculations are costly, three-dimensional shape collaborative optimization is difficult to implement, and data-driven design lacks physical constraints, making it impossible to generate reliable optimization results.
Parametric modeling is performed using shape transformation and singular value decomposition methods. A low-order aerodynamic model and a multilayer perceptron neural network are constructed. A gradient-based sequential least squares programming algorithm is introduced, combined with modal space Euclidean distance constraints, to ensure the geometric rationality and manufacturability of the optimization solution.
It has realized an automated design process for rotor aerodynamic shape optimization, which greatly improves design efficiency, shortens the R&D cycle, realizes three-dimensional shape collaborative optimization, reduces computing costs, and generates highly manufacturable optimization results.
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Figure CN122174373B_ABST