一种数据驱动的气动热预示模型  

A data-driven aeroheating prediction model

作　　者：王泽 王梓伊 王旭[1] 宋述芳[1] 张伟伟[1] WANG Ze;WANG Ziyi;WANG Xu;SONG Shufang;ZHANG Weiwei(School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China;China Aerodynamics Research and Development Center,Mianyang 621000,China)

机构地区：[1]西北工业大学航空学院,西安710072 [2]中国空气动力研究与发展中心,绵阳621000

出　　处：《空气动力学学报》2023年第5期12-19,I0001,共9页Acta Aerodynamica Sinica

摘　　要：高效、高精度的气动热预示是高超声速飞行器设计的关键。然而,随着高超声速飞行器外形的日益复杂化和设计周期的不断缩紧,现有方法已很难满足高效精准的气动热预示。本文基于边界层理论和支持向量机发展了一种数据驱动的当地化气动热预示建模方法。首先,通过求解Euler方程获得边界层外缘信息,采用RANS方法计算热流分布样本;然后,通过设计的特征选择方法确定边界层外缘特征;最后,利用支持向量机构建气动热预示模型,实现边界层外缘特征与壁面热流的映射。对双椭球和二级压缩面的热流预示结果表明,该模型考虑了非均匀分布壁面温度等边界条件,具有较高的预示精度和良好的外推与泛化性能,典型位置热流预示结果和RANS计算结果的相对误差均小于5%。同时,以双椭球上表面中心线热流预示为例,对比传统POD降阶方法,发现该模型的预示精度更高,外推状态下预示精度较POD方法提升了4倍以上。Aeroheating prediction with high efficiency and high accuracy is crucial for the design of hypersonic vehicles.However,the increasing shape complexity and tight design period of hypersonic vehicles make it difficult for existing methods to meet the requirements of efficient and accurate aeroheating prediction.In this study,a localized data-driven modeling method for rapid aeroheating prediction is developed based on the boundary layer theory and the support vector machine.Firstly,the outer edge boundary layer information is obtained by solving the Euler equations,and the RANS method is used to generate samples of heat flux distributions.Then,a feature selection approach is developed to acquire the outer edge boundary layer features.Finally,the support vector machine is used to construct the aeroheating prediction model to achieve the mapping between the outer edge boundary layer features and the heat flux on the wall.Results of the aeroheating prediction for a double ellipsoid and a two-stage compression surface show that the model considers local boundary conditions such as the non-uniform wall temperature,and has high accuracy as well as good extrapolation and generalization capability.The relative errors of heat flux between the model prediction and the RANS calculation are less than 5%.Moreover,for the aeroheating flux prediction along the center line on the upper surface of the double ellipsoid,the prediction ability of the present model is better than the traditional proper orthogonal decomposition(POD)reduction method,especially the prediction accuracy of the present model in the extrapolation regime is more than four times higher than that of the POD reduction model.

关 键 词：气动热 边界层理论 支持向量机 特征选择 当地化模型 

分 类 号：V211.22[航空宇航科学与技术—航空宇航推进理论与工程]