基于门控神经网络的大迎角非定常气动力建模OACSTPCD
Gated Neural Network-Based Unsteady Aerodynamic Modeling for Large Angles of Attack
基于少量实验或仿真数据构建未知状态下的大迎角非定常气动力模型,能够极大地提高飞机非定常空气动力学设计和飞行动力学分析的效率.针对传统气动模型通用性差以及智能模型泛化能力差的问题,提出了一种基于门控神经单元的智能气动力建模方法.充分利用门控神经单元的时间记忆特性,增强了学习和训练过程对非线性流场的表征能力,提高了整个预测模型的泛化能力.以NACA0015翼型为研究对象,在机动飞行条件下对其非定常气动力进行了预测和验证,结果表明本文构建的模型具有良好…查看全部>>
Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis.In this paper,aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models,an intelligent aerodynamic modeling method based on gated neural units …查看全部>>
邓永涛;程诗信;米百刚
北京空间机电研究所,北京 100094,中国沈阳飞机设计研究所扬州协同创新研究院有限责任公司,扬州 110066,中国西北工业大学航空学院,西安 710072,中国
大迎角非定常气动力建模门控神经网络泛化能力
large angle of attackunsteady aerodynamic modelinggated neural networksgeneralization ability
《南京航空航天大学学报（英文版）》 2024 (4)
432-443,12
This work was supported in part by the National Natural Science Foundation of China(No.12202363).
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