Research progress on the fusion of data obtained by high-speed wind tunnels, CFD and model flight

TANG Zhigong; YUAN Xianxu; QIAN Weiqi; XIAO Hanshan; BI Lin; WANG Wenzheng

doi:10.7638/kqdlxxb-2023.0096

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Research progress on the fusion of data obtained by high-speed wind tunnels, CFD and model flight

更新时间：2026-05-28

- Research progress on the fusion of data obtained by high-speed wind tunnels, CFD and model flight
- Vol. 41, Issue 8, Pages: 44-58(2023)
- 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：
- 基金信息：
- DOI：10.7638/kqdlxxb-2023.0096
  CLC： V211
- Published：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

LI Y X. Research on the development history of US hypersonic aircrafts[D]. Harbin: Harbin Institute of Technology, 2016 (in Chinese).

LI W, DONG D F, HOU X. Development trends and focus of Russian hypersonic vehicles[J]. Aerodynamic Missile Journal, 2021(11): 46-51. (in Chinese)

EGGERS T, LONGO J, HRSCHGEN M, et al. The hypersonic flight experiment SHEFEX: AIAA 2005-3294 [R]. Reston: AIAA, 2005.

KIMMEL R L, ADAMCZAK D W, BORG M P, et al. HIFiRE-1 and-5 flight and ground tests[C]// 2018 AIAA Aerospace Sciences Meeting, Kissimmee, Florida. Reston, Virginia: AIAA, 2018: AIAA2018-0056. doi: 10.2514/6.2018-0056

BULMER B M. Study of base pressure in laminar hypersonic flow: re-entry flight measurements[J]. AIAA Journal, 1975, 13(10): 1340-1348. DOI: 10.2514/3.60546

TU G H, CHEN J Q, YUAN X X, et al. Progress in flight tests of hypersonic boundary layer transition[J]. Acta Mechanica Sinica, 2021, 37(11): 1589-1609. DOI: 10.1007/s10409-021-01153-4

TANG Z G, XU X B, YANG Y G, et al. Research progress on hypersonic wind tunnel aerodynamic testing techniques[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(1): 86-97. (in Chinese)

BI L, LI Z Q, TANG Z G, et al. Study on the efficiency of a new central-type diffuser for hypersonic low density wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2019, 33(6): 28-33. (in Chinese)

LIU H S, XU X, JIANG H, et al. The application of the stress wave force balance in shock tunnel in China[J]. Journal of Experiments in Fluid Mechanics, 2006, 20(1): 36-39. (in Chinese)

CHEN H, YUAN X X, WANG T T, et al. Advances in viscous adaptive Cartesian grid methodology of NNW Project[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(9): 625732. (in Chinese)

CHEN J Q, WU X J, ZHANG J, et al. FlowStar: general unstructured-grid CFD software for national numerical windtunnel (NNW)project[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(9): 1-22. (in Chinese)

ZHAO Z, HE L, HE X Y. Design of general CFD software PHengLEI[J]. Computer Engineering and Science, 2020, 42(2): 210-219. (in Chinese)

OU C, JI H L, XIAO H S, et al. Key problems in structure and thermal protection for MF-1 model testing flight vehicle[J]. Acta Aerodynamica Sinica, 2017, 35(5): 742-749. (in Chinese)

YANG P, TANG Z G, CHEN J Q, et al. Temporal evolution of wavepackets on the windward side of an inclined hypersonic cone under a flight condition[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(S1): 98-106. (in Chinese)

YANG Q T, ZHOU Y, YUAN X X, et al. Surface pressure and temperature measurement technology in MF-1 modelling flight test[J]. Acta Aerodynamica Sinica, 2017, 35(5): 732-741. (in Chinese)

LEE S B, DUAN M C, TU G H, et al. Investigation on transition correlation of conical boundary layer in hypersonic wind tunnel experiments[J]. Acta Astronautica, 2022, 201: 329-339. DOI: 10.1016/j.actaastro.2022.09.029

GAN Q, HARRIS C J. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion[J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(1): 273-279. DOI: 10.1109/7.913685

GAO J B, HARRIS C J. Some remarks on Kalman filters for the multisensor fusion[J]. Information Fusion, 2002, 3(3): 191-201. DOI: 10.1016/S1566-2535(02)00070-2

ZIMMERMANN R, HAN Z H. Simplified cross-correlation estimation for multifidelity surrogate cokriging models[J]. Advances and Applications in Mathematical Sciences, 2010.

HAN Z H, GÖRTZ S. Hierarchical kriging model for variable-fidelity surrogate modeling[J]. AIAA Journal, 2012, 50(9): 1885-1896. DOI: 10.2514/1.j051354

PERDIKARIS P, RAISSI M, DAMIANOU A, et al. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017, 473(2198): 20160751. DOI: 10.1098/rspa.2016.0751

LEE S, DIETRICH F, KARNIADAKIS G E, et al. Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusion[J]. Interface Focus, 2019, 9(3): 20180083. DOI: 10.1098/rsfs.2018.0083

MENG X H, KARNIADAKIS G E. A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems[J]. Journal of Computational Physics, 2020, 401: 109020. DOI: 10.1016/j.jcp.2019.109020

HE L, QIAN W Q, ZHAO T, et al. Multi-fidelity aerodynamic data fusion with a deep neural network modeling method[J]. Entropy, 2020, 22(9): 1022. DOI: 10.3390/e22091022

MENG X H, BABAEE H, KARNIADAKIS G E. Multi-fidelity Bayesian neural networks: Algorithms and applications[J]. Journal of Computational Physics, 2021, 438: 110361. DOI: 10.1016/j.jcp.2021.110361

JOSPIN L V, LAGA H, BOUSSAID F, et al. Hands-on Bayesian neural networks—a tutorial for deep learning users[J]. IEEE Computational Intelligence Magazine, 2022, 17(2): 29-48. DOI: 10.1109/MCI.2022.3155327

YANG L, MENG X H, KARNIADAKIS G E. B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data[J]. Journal of Computational Physics, 2021, 425: 109913. DOI: 10.1016/j.jcp.2020.109913

ZHOU Y, YANG Q T, WANG A L, et al. Estimation of surface heat flux for MF-1 flight test[J]. Acta Aerodynamica Sinica, 2019, 37(6): 938-944. (in Chinese)

YUAN X X, HE K, CHEN J Q, et al. Preliminary transition research analysis of MF-1[J]. Acta Aerodynamica Sinica, 2018, 36(2): 286-293. (in Chinese)

JEWELL J S, JULIANO T J, STANFIELD S, et al. HIFiRE-1 and -5 flight and ground tests: AIAA 2018-0056[R]. Reston: AIAA, 2018.

KIMMEL R L, PRABHU D K. HIFiRE-1 turbulent shock boundary layer interaction-flight data and computations[C]// Proceedings of the 45th AIAA Fluid Dynamics Conference, Dallas, TX. Reston, Virginia: AIAA, 2015: AIAA2015-2639. doi: 10.2514/6.2015-2639

LI X L, FU D X, MA Y W, et al. Direct numerical simulation of shock/turbulent boundary layer interaction in a supersonic compression ramp[J]. Science China Physics, Mechanics and Astronomy, 2010, 53(9): 1651-1658. DOI: 10.1007/s11433-010-4034-x

TONG F L, YU C P, TANG Z G, et al. Numerical studies of shock wave interactions with a supersonic turbulent boundary layer in compression corner: turning angle effects[J]. Computers Fluids, 2017, 149: 56-69. DOI: 10.1016/j.compfluid.2017.03.009

SHEN P F, LIU P X, SUN D, et al. Statistical characteristics of skin friction of shock wave/turbulent boundary layer interaction in hollow cylinder-flare configuration at Mach 6[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(1): 87-102. (in Chinese)

SUN D, LIU P X, SHEN P F, et al. Direct numerical simulation of shock wave/turbulent boundary layer interaction in hollow cylinder-flare configuration at Mach number 6[J]. Acta Aeronauticaet Astronautica Sinica, 2021, 42(12): 124681(in chinese).

NEUMANN R D. Special topics in hypersonic flow, aerodynamic problems of hypersonic vehicles[R]. AGARD Lecture Series No. 42, von Karman Inst. Brussels, 1972

HOLDEN M S. Shock wave-turbulent boundary layer interaction in hypersonic flow[C]// 10th Aerospace Sciences Meeting, San Diego, CA, USA. Reston, Virigina: AIAA, 1972: AIAA1972-74. doi: 10.2514/6.1972-74

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Intelligent fusion method of multi-source aerodynamic data for flight tests

Numerical and experimental study on flutter characteristics of a wing with drag rudder

Research on rolling instability of typical high-speed aircraft

Numerical and experimental study on opposing jet in hypersonic flow

Flight tests and predictions of sonic boom signature for supersonic aircraft

Related Author

WANG Xu

NING Chenjia

WANG Wenzheng

ZHANG Weiwei

LI Yongchang

HAN Yizhe

DAI Yuting

YANG Chao

Related Institution

School of Aeronautics, Northwestern Polytechnical University, Xi’an

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu

School of Aeronautic Science and Engineering, Beihang University, Beijing

Tianmushan Laboratory, Hangzhou

Hangzhou International Innovation Institute, Beihang University, Hangzhou

AI问答

⁰