高马赫数气动热环境数据融合方法与展望  

Research and expectation of data fusion method for high mach aerodynamic heat

作　　者：王润 刘晋 张青青 尹琰鑫 屈强 Wang Run;Liu Jin;Zhang Qingqing;Yin Yanxin;Qu Qiang(Beijing Institute of Astronautical Systems Engineering,Beijing 100076,China;China Academy of Launch Vehicle Technology,Beijing 100076,China)

机构地区：[1]北京宇航系统工程研究所,北京100076 [2]中国运载火箭技术研究院,北京100076

出　　处：《空天技术》2024年第3期33-41,共9页Aerospace Technology

摘　　要：介绍了目前高马赫数飞行器气动热研究面临的问题以及研究方法,分析了工程计算、数值模拟和风洞试验等常用评估手段的优劣,并以当前方法的局限性为切入点,引出了机器学习和数据融合方法在气动热领域的应用。汇总了当前气动热领域的数据融合研究成果,将应用方向大致分为湍流模型优化、气动热快速评估、不确定度辨识和天地相关性研究三类。虽然目前气动热领域的数据融合仍处于探索阶段,但是鉴于气动热设计过程中产生了大量的多源数据,为数据融合的发展提供了广阔的应用空间。将气动热环境设计与数据融合方法相结合,既有助于提升数据库计算效率,也可以归纳并发掘新的机理,推进气动专业领域发展。Current methods for the prediction of high mach aerodynamic heat are introduced in this paper,including engineering calculation,computational fluid dynamics(CFD)and wind tunnel experiment.The characteristics of these methods are evaluated and the applications of machine learning and data fusion are introduced to cover the shortage of traditional methods.This paper summarizes the achievements of data fusion method for aerodynamic heat,which are divided into turbulence modeling,quick evaluation of aerodynamic heat,and the uncertainties and correlations between experiment on the ground and the statues of real flight.Recently,data fusion for aerodynamic heat is still in the exploratory phase,however,due to the large amount of multi-source data,data fusion method has broad application area.The combination of data fusion and aerodynamic heat would not only improve the efficiency for database calculation,but also explore the new mechanism for high mach aerodynamic heat,and promote the development of aerodynamic field.

关 键 词：高马赫数 气动热 机器学习 数据融合 飞行器 

分 类 号：V11[航空宇航科学与技术—人机与环境工程]