航空科学技术2024，Vol.35Issue(11)：13-18,6.DOI:10.19452/j.issn1007-5453.2024.11.002
基于集成学习的飞机气动力快速预测方法研究
Research on Rapid Prediction Method of Aircraft Aerodynamics Based on Ensemble Learning
摘要
Abstract
The demand for aerodynamic shape optimization efficiency in modern aircraft design is constantly increasing.Traditional aerodynamic force acquisition methods such as the wind tunnel experiment or the CFD numerical simulation have high costs and low efficiency.Exploring efficient aerodynamic force acquisition methods is of great significance in reducing wind tunnel testing or numerical simulation costs and improving aircraft iterative design efficiency.A fast prediction method for aircraft aerodynamics based on ensemble learning is proposed in this article.The linear regression model,multi-layer perceptron model,and gradient boosting model are stacked to predict the aerodynamic force coefficients of the flying wing layout drones with different wing span lengths,root chord ratios,and tip chord lengths at different angles of attack.The results show that the established ensemble learning model can predict the aerodynamic coefficients of aircraft quickly and accurately.The mean square errors of the lift and drag coefficients in the test sets are 0.208×10-4 and 0.424×10-5,respectively,with the mean absolute errors of 0.27×10-2 and 0.1379×10-2,the fitting degrees of 0.9994976 and 0.9691,and a prediction time of 0.8s,which is only 1/4500 of the calculation time of the panel method,which improves the efficiency of aircraft aerodynamic shape design effectively.关键词
气动力/集成学习/快速预测/梯度提升模型/堆叠法Key words
aerodynamics/ensemble learning/quick prediction/Gradient Boosting model/stacking method分类
航空航天引用本文复制引用
刘哲,郭承鹏,李鸿岩,崔榕峰..基于集成学习的飞机气动力快速预测方法研究[J].航空科学技术,2024,35(11):13-18,6.基金项目
航空科学基金(2022Z006026004,2023M071027001) Aeronautical Science Foundation of China(2022Z006026004,2023M071027001) （2022Z006026004,2023M071027001）
