空气动力学学报2025，Vol.43Issue(9)：28-38,11.DOI:10.7638/kqdlxxb-2025.0026
一种基于GRU神经网络的航空发动机叶片颤振快速预测方法
A rapid prediction method for flutter of aero-engine blades based on modal shape decomposition combined with GRU neural network
摘要
Abstract
Aeroengine blade flutter poses a critical safety hazard induced by aeroelastic instability,with traditional prediction methodologies requiring extensive unsteady flow field simulations that entail substantial computational time.This paper proposes a method for calculating aerodynamic loads on aero-engine blades based on modal shape decomposition combined with a gated recurrent unit(GRU)neural network model,and applies it to rapid estimation of aerodynamic damping and flutter analysis of aero-engine blades.We first perform bending-torsion decomposition on the blade's natural vibration modes and calculate corresponding aerodynamic modal forces for both bending and torsion modes.Subsequently,temporal GRU neural networks are employed to establish mapping relationships between aerodynamic modal forces and generalized motion variables for both bending and torsion modes.These established mappings are then applied to compute aerodynamic damping and predict flutter for blades with different bending-torsion ratios within specific frequency ranges.Using the NASA Rotor67 rotor model as a case study,the proposed aerodynamic modal force model for bending-torsion modes is validated,and flutter analysis is implemented for blades with varying bending-torsion ratio modes.Results demonstrate that the model can accurately estimate unsteady aerodynamic loads on aero-engine blades within certain frequency ranges and enable efficient aerodynamic damping estimation for blades with different bending-torsion ratio vibration modes.The method developed in this study significantly accelerates the flutter design process of aero-engine blades.关键词
颤振预测/模态振型分解/门控循环单元/神经网络/气动阻尼Key words
flutter prediction/mode shape decomposition/gated recurrent unit/neural network/aerodynamic damping分类
航空航天引用本文复制引用
姜嘉宇,黄璜,陈美宁,曹博超..一种基于GRU神经网络的航空发动机叶片颤振快速预测方法[J].空气动力学学报,2025,43(9):28-38,11.基金项目
航空发动机及燃气轮机基础科学中心项目(P2023-B-Ⅱ-001-001) （P2023-B-Ⅱ-001-001）
教育部"春晖计划"合作科研项目(202200745) （202200745）
