航空科学技术2025，Vol.36Issue(2)：102-110,9.DOI:10.19452/j.issn1007-5453.2025.02.009
基于QPSO-LSTM神经网络建立非定常气动模型的方法
Method of Predicting Unsteady Aerodynamic Force Based on QPSO-LSTM Neural Network
摘要
Abstract
Aircraft aerodynamic parameters are highly nonlinear and exhibit significant unsteady characteristics,making traditional modeling approaches complex and technically demanding.Leveraging artificial intelligence(AI)methods can bypass these complexities,lower the technical barriers,and enhance modeling efficiency.This paper proposes a method of predicting unsteady aerodynamic force by using QPSO-LSTM neural network.The QPSO-LSTM model is constructed by initially employing the LSTM algorithm as the base neural network model,followed by the application of QPSO algorithm to globally optimize the neural network hyperparameters.The hyperparameters include the number of neurons per network layer,historical length of training data,and batch size during the training process.To validate the effectiveness of the modeling approach,the neural network is trained by using aerodynamic force data obtained from numerical simulations of NACA0012 airfoil under various periodic pitching motion conditions.The results indicate that QPSO algorithm is a good choice for optimizing LSTM neural network hyperparameters,which could effectively search for the global optimal solution and avoid human factors to influence the results in setting the hyperparameters.The QPSO-LSTM neural network also demonstrates the capability of precisely predicting unsteady aerodynamic force coefficient across various flight conditions,solely relying on limited flight input parameter.This feature could make the modeling method to be conveniently deployed.Furthermore,compared to conventional RNN and LSTM models,the QPSO-LSTM model demonstrates superior accuracy and enhances generalization capabilities in predicting unsteady aerodynamic force coefficients in both interpolation and extrapolation scenarios.This approach holds significant potential for applications in unsteady aerodynamic force prediction within the aerospace sector.关键词
QPSO/LSTM/神经网络/非定常/气动力建模Key words
QPSO/LSTM/neural network/unsteady/modeling aerodynamic force分类
航空航天引用本文复制引用
魏小峰,魏巍,李鹏..基于QPSO-LSTM神经网络建立非定常气动模型的方法[J].航空科学技术,2025,36(2):102-110,9.基金项目
上海市浦江人才计划(22PJ1420900) Shanghai Pujiang Program(22PJ1420900) （22PJ1420900）
