高速空气动力学三大手段数据融合研究进展

唐志共; 袁先旭; 钱炜祺; 肖涵山; 毕林; 王文正

doi:10.7638/kqdlxxb-2023.0096

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高速空气动力学三大手段数据融合研究进展

研究专栏——庆祝国防科技大学建校70周年 | 更新时间：2026-05-28

- 高速空气动力学三大手段数据融合研究进展
- Research progress on the fusion of data obtained by high-speed wind tunnels, CFD and model flight
- 空气动力学学报 2023年41卷第8期页码：44-58
- 作者机构：
  
  1. 中国空气动力研究与发展中心,绵阳,621000
  2. 空天飞行空气动力科学与技术全国重点实验室,绵阳,621000
  3. 电子科技大学,成都,611731
- 作者简介：
  
  唐志共（1965-），男，博士，研究员，研究方向：高速空气动力学研究与应用. E-mail： tangzhigong@126.com
  王文正* （1968-），男，四川简阳人，研究员，博士，研究方向：飞行力学与飞行控制. E-mail: wangwz@uestc.edu.cn
- 基金信息：
  
  国家数值风洞工程（ NNW）
- DOI：10.7638/kqdlxxb-2023.0096
  中图分类号： V211
- 纸质出版：2023
- 稿件说明：
移动端阅览
唐志共, 袁先旭, 钱炜祺, 等. 高速空气动力学三大手段数据融合研究进展[J]. 空气动力学学报, 2023, 41(8): 44−58.

TANG Zhigong, YUAN Xianxu, QIAN Weiqi, et al. Research progress on the fusion of data obtained by high-speed wind tunnels, CFD and model flight[J]. 2023, 41(8): 44-58.
唐志共, 袁先旭, 钱炜祺, 等. 高速空气动力学三大手段数据融合研究进展[J]. 空气动力学学报, 2023, 41(8): 44−58. DOI： 10.7638/kqdlxxb-2023.0096

TANG Zhigong, YUAN Xianxu, QIAN Weiqi, et al. Research progress on the fusion of data obtained by high-speed wind tunnels, CFD and model flight[J]. 2023, 41(8): 44-58. DOI： 10.7638/kqdlxxb-2023.0096.

摘要

风洞试验、数值计算和模型飞行试验三大手段的深度融合，是开展新一代高速飞行器研究的必然需求。本文重点介绍了高速风洞试验设备、数值计算软硬件建设和航天模型飞行试验能力建设情况，以及自主研制的表面温度、热流、脉动压力、摩阻等飞行试验测量技术；并根据气动数据融合特点，提出了一种基于气动数据和物理模型相关度的融合准则，发展了基于组合深度神经网络的气动数据融合方法，解决了不同来源数据之间的数据关联问题，大幅提升了融合数据的可信度，在某高速飞行器俯仰力矩系数和头罩典型构型的气动热数据天地关联方面得到成功应用；综合运用三大手段，开展了高速激波-边界层干扰基础流动问题研究，建立了激波-边界层干扰力/热载荷天地相关性经验公式，修正了压力-热流关联关系，并首次证实了分离泡低频振荡现象在真实飞行条件下客观存在。

Abstract

The deep integration of wind tunnel experiments

numerical calculations

and model flight tests is an inevitable requirement for developing future high-speed vehicle. This article focuses on constructing high-speed wind tunnel facilities

numerical calculation software and hardware

model flight testing capabilities

and self-developed flight test techniques for measuring the surface temperature

heat flux

pressure

friction drag

etc. Based on the characteristics of aerodynamic data fusion

an aerodynamic data fusion method

based on the correlation between aerodynamic data and physical models as well as deep learning

was developed to solve the problem of data association between different data sources

significantly improving the datas reliability. The data fusion method has been successfully applied in the flight-ground correlation of the pitching moment coefficient and aerothermodynamic data for high-speed vehicle. A comprehensive study of the high-speed shock/boundary-layer interaction problem using various methods results in an empirical formula of the interaction force/thermal load

a correction to the pressure/heat flow correlation

and the verification of the low-frequency oscillation of the separation bubble under real flight conditions.

关键词

Keywords

references

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