收稿日期:
2023-11-02
修回日期:
2023-11-27
接受日期:
2023-12-25
出版日期:
2024-05-15
发布日期:
2024-01-04
通讯作者:
陈荣钱
E-mail:rqchen@xmu.edu.cn
基金资助:
Jiaqi LIU, Rongqian CHEN(), Jinhua LOU, Xu HAN, Hao WU, Yancheng YOU
Received:
2023-11-02
Revised:
2023-11-27
Accepted:
2023-12-25
Online:
2024-05-15
Published:
2024-01-04
Contact:
Rongqian CHEN
E-mail:rqchen@xmu.edu.cn
Supported by:
摘要:
针对高速直升机旋翼翼型的气动优化问题,建立了一种基于深度学习的翼型多目标气动优化框架。首先,搭建深度神经网络预测旋翼翼型气动力系数的代理模型。以SC1095旋翼翼型作为基准翼型,采用CST方法对翼型进行参数化表示,采用拉丁超立方采样方法生成用于深度神经网络训练的翼型数据集。综合考虑直升机前飞、机动和悬停状态等多设计点的气动性能,开展了基于深度神经网络的代理模型结合多岛遗传算法的高速直升机旋翼翼型多目标气动优化设计。优化结果显示:相比于基准翼型,在没有损失悬停和机动性能的前提下,优化翼型显著改善了前飞性能。分别采用基准翼型和优化翼型建立共轴刚性旋翼,计算并分析前飞状态下基准旋翼和优化旋翼的气动性能,结果表明优化翼型可以显著地提升高速直升机旋翼的气动性能。
中图分类号:
柳家齐, 陈荣钱, 楼锦华, 韩旭, 吴昊, 尤延铖. 基于深度学习的高速直升机旋翼翼型气动优化设计[J]. 航空学报, 2024, 45(9): 529828-529828.
Jiaqi LIU, Rongqian CHEN, Jinhua LOU, Xu HAN, Hao WU, Yancheng YOU. Aerodynamic shape optimization of high-speed helicopter rotor airfoil based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(9): 529828-529828.
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