基于数据同化方法修正RBF神经网络的高维气动力建模预测  

High-Dimensional Aerodynamic Modeling Prediction Based on Modified RBFNeural Network with Data Assimilation

作　　者：张迎 张鑫[2] 张卫国[2] 邓子辰 ZHANG Ying;ZHANG Xin;ZHANG Weiguo;DENG Zichen(School of Aeronautics,Northwestern Polytechnical University,Xi′an 710072,China;Rotor Aerodynamics Key Laboratory,China Aerodynamics Research and Development Center,Mianyang 621000,China)

机构地区：[1]西北工业大学航空学院,陕西西安710072 [2]中国空气动力研究与发展中心旋翼空气动力学重点实验室,四川绵阳621000

出　　处：《气体物理》2024年第3期46-54,共9页Physics of Gases

基　　金：国家自然科学基金(12172291);飞行器基础布局全国重点实验室基金(D5150240011)。

摘　　要：通过数据同化方法修正径向基函数(radial basis function,RBF)神经网络,以提高高维气动力的建模精度。通过在传统RBF神经网络的核函数中引修正量γ,使用EnKF滤波数据同化算法修正该矫正因子,并将其应用于CRA309旋翼翼型的高维气动力建模预测中。结果发现数据同化方法采用非侵入的方式,在不破坏神经网络整体架构的情况下仅对核函数的矫正因子进行修正,大幅减少优化参数与变量,显著提升了RBF神经网络的建模精度和效率。将修正后的RBF神经网络模型应用于高维气动力建模中,用仿真数据替代对气动力参数进行预测。设计结果验证了预测模型的可行性,在风洞试验数据较少的情况下对提高试验数据的利用效率具有一定的工程实用价值。In this paper,the radial basis function(RBF)neural network was modified by data assimilation method to improve the modeling accuracy of high-dimensional aerodynamics.A correction factorγwas introduced into the kernel function of the traditional RBF neural network.The correction factor was corrected by using the EnKF data assimilation algorithm,and it was applied to the high-dimensional aerodynamic modeling prediction of the CRA309 rotor airfoil.The data assimilation method adopts a non-intrusive method to correct only the correction factor of the kernel function without destroying the overall architecture of the neural network,which greatly reduces the optimization parameters and variables,and significantly improves the modeling accuracy and efficiency of the RBF neural network.The modified RBF neural network model was then applied to high-dimensional aerodynamic modeling,and the aerodynamic parameters were predicted by simulation data instead.The design results verify the feasibility of the prediction model.In the case of less wind tunnel test data,it has certain engineering practical value to improve the utilization efficiency of test data.

关 键 词：RBF神经网络 数据同化 气动力建模 泛化能力 

分 类 号：O355[理学—流体力学]