基于增量学习的非定常气动力参数化降阶模型  

Parametric reduced-order model of unsteady aerodynamics based on incremental learning algorithm

作　　者：陈志强 刘战合 苗楠 冯伟[2] CHEN Zhiqiang;LIU Zhanhe;MIAO Nan;FENG Wei(School of Aeronautical Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046.China;School of Electromechanical Engineering,Henan University of Technology,Zhengzhou 450046.China)

机构地区：[1]郑州航空工业管理学院航空工程学院,郑州450046 [2]河南工业大学机电工程学院,郑州450046

出　　处：《航空学报》2021年第7期336-347,共12页Acta Aeronautica et Astronautica Sinica

基　　金：国家自然科学基金(11702255);河南省科技攻关计划(212102210052,212102210334,202102210267,202102210509,202102210288)。

摘　　要：气动降阶模型(ROM)是预测非定常气动力的有效工具,具有高精度和低计算成本的优点,近年来许多研究证实了该方法的有效性。但是关于飞行参数变化时,ROM的鲁棒性还需要进一步提高。为了提高ROM对不同飞行参数下的气动力预测能力,提出了基于最小二乘支持向量回归(LS-SVR)和增量学习算法的参数化降阶模型。LS-SVR是一种具有良好泛化能力的回归方法,基于LS-SVR的增量学习算法的主要贡献是在增加新样本集时,不需要重新学习整个数据集。为说明该方法的有效性,基于两自由度NACA64A010翼型构建参数化非定常气动力降阶模型。为了训练气动力输入和相应输出之间的关系,将马赫数和迎角作为附加的模型输入。仿真结果表明,该降阶模型能够准确描述气动力和气动弹性系统在不同飞行参数下的动态特性。The aerodynamic Reduced-Order Model(ROM)is a useful tool in the prediction of nonlinear unsteady aerodynamics with reasonable accuracy and low computational cost.The efficacy of this method has been validated by recent studies.However,the robustness of ROMs with respect to flight parameter variations should be further improved.To enhance the prediction capability of ROMs for varying flight parameters,this paper presents two parametric reduced-order models based on the Least Squares Support Vector Regression(LS-SVR)and the incremental learning algorithm.LS-SVR is a class of regression methods with good generalization ability,and the main contribution of the incremental learning algorithm based on LS-SVR is that it is not necessary to relearn the whole data set while the new sample sets are incremented.To illustrate the approach,we construct the parametric unsteady aerodynamic ROMs of the NACA64 A010 airfoil model with two degrees of freedom.The Mach number and angle of attack are considered as the additional model inputs to train the relationship between aerodynamic inputs and the corresponding outputs.It is demonstrated that the model can accurately capture the dynamic characteristics of aerodynamic and aeroelastic systems for varying flight parameters.

关 键 词：非定常气动力 参数化降阶模型 最小二乘支持向量回归 增量学习算法 颤振 

分 类 号：V211.3[航空宇航科学与技术—航空宇航推进理论与工程]