一种数据驱动的气动热预示模型

王泽; 王梓伊; 王旭; 宋述芳; 张伟伟

doi:10.7638/kqdlxxb-2022.0010

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一种数据驱动的气动热预示模型

研究论文 | 更新时间：2026-05-28

- 一种数据驱动的气动热预示模型
- A data-driven aeroheating prediction model
- 空气动力学学报 2023年41卷第5期页码：12-19
- 作者机构：
  
  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
- 作者简介：
  
  王泽（1998-），男，河北保定人，硕士研究生，研究方向：气动热建模与热气动弹性. E-mail：bdqywz123@163.com
  张伟伟*（1979-），男，教授，研究方向：流体力学、气动弹性力学. E-mail：aeroelastic@nwpu.edu.cn
- 基金信息：
- DOI：10.7638/kqdlxxb-2022.0010
  中图分类号： V211.22
- 纸质出版：2023
- 稿件说明：
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王泽, 王梓伊, 王旭, 等. 一种数据驱动的气动热预示模型[J]. 空气动力学学报, 2023, 41(5): 12−19.

WANG Ze, WANG Ziyi, WANG Xu, et al. A data-driven aeroheating prediction model[J]. 2023, 41(5): 12-19.
王泽, 王梓伊, 王旭, 等. 一种数据驱动的气动热预示模型[J]. 空气动力学学报, 2023, 41(5): 12−19. DOI： 10.7638/kqdlxxb-2022.0010

WANG Ze, WANG Ziyi, WANG Xu, et al. A data-driven aeroheating prediction model[J]. 2023, 41(5): 12-19. DOI： 10.7638/kqdlxxb-2022.0010.

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。

Abstract

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

Keywords

references

MCNAMARA J J, FRIEDMANN P P. Aeroelastic and aerothermoelastic analysis in hypersonic flow: past, present, and future[J]. AIAA Journal, 2011, 49(6): 1089-1122. DOI:10.2514/1.J050882[LinkOut]

LIANG W, JIN H, MENG S H, et al. Research progress on new thermal protection mechanism of hypersonic vehicles[J]. Journal of Astronautics, 2021, 42(4): 409-424. (in Chinese)[万方]

PENG Z Y, SHI Y L, GONG H M, et al. Hypersonic aeroheating prediction technique and its trend of development[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(1): 325-345. (in Chinese)[万方]

LUO C T, HU Z M, LIU Y F, et al. Research progress on ground-to-flight correlation of aerodynamic force and heating data from hypersonic wind tunnels[J]. Journal of Experiments in Fluid Mechanics, 2020, 34(3): 78-89. (in Chinese)[万方]

WANG Z Y, ZHANG W W, LIU L. Review of simulation methods of hypersonic aerothermoelastic problems[J]. Physics of Gases, 2020, 5(6): 1-15. (in Chinese)[知网中文][知网英文]Doi: 10.19527 / j.cnki.2096-1642.0864

YU C Z, LIU W H. Research status of aeroheating prediction technology for hypersionic aircraft[J]. Aeronautical Science Technology, 2021, 32(2): 14-21. (in Chinese)[知网中文][知网英文]Doi: 10.19452 / j.i ssn1007-5453.2021.02.002

LV L L, ZHANG W W, YE Z Y. Predicting heating distributions for hypersonic reentry bodies[J]. Chinese Journal of Applied Mechanics, 2006, 23(2): 259-262. (in Chinese)[万方]

MCNAMARA J J, FRIEDMANN P P, POWELL K G, et al. Aeroelastic and aerothermoelastic behavior in hypersonic flow[J]. AIAA Journal, 2008, 46(10): 2591-2610. DOI:10.2514/1.36711[LinkOut]

FALKIEWICZ N J, CESNIK C E S, CROWELL A R, et al. Reduced-order aerothermoelastic framework for hypersonic vehicle control simulation[J]. AIAA Journal, 2011, 49(8): 1625-1646. DOI:10.2514/1.J050802[LinkOut]

CHEN X, LIU L, YUE Z J. Reduced order aerothermodynamic modeling research for hypersonic vehicles based on proper orthogonal decomposition and surrogate method[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(2): 462-472. (in Chinese)[万方]

CHEN X, LIU L, LONG T, et al. A reduced order aerothermodynamic modeling framework for hypersonic vehicles based on surrogate and POD[J]. Chinese Journal of Aeronautics, 2015, 28(5): 1328-1342. DOI:10.1016/j.cja.2015.06.024[LinkOut]

CHEN X, LIU L, ZHOU S D, et al. Adding-point strategy for reduced-order hypersonic aerothermodynamics modeling based on fuzzy clustering[J]. Chinese Journal of Mechanical Engineering, 2016, 29(5): 983-991. DOI:10.3901/cjme.2016.0128.016[LinkOut]

NIE C S, HUANG J D, WANG X, et al. Fast aeroheating prediction method for complex shape vehicles based on proper orthogonal decomposition[J]. Acta Aerodynamica Sinica, 2017, 35(6): 760-765. (in Chinese)[万方]

ZHANG Z C, GAO T Y, ZHANG L, et al. Aeroheating agent model based on radial basis function neural network[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 297-306. (in Chinese)[万方]

MEHRKANOON S, SUYKENS J A K. Learning solutions to partial differential equations using LS-SVM[J]. Neurocomputing, 2015, 159: 105-116. DOI:10.1016/j.neucom.2015.02.013[LinkOut]

YANG L X, YANG S Y, LI S J, et al. Coupled compressed sensing inspired sparse spatial-spectral LSSVM for hyperspectral image classification[J]. Knowledge-Based Systems, 2015, 79(5): 80-89. DOI:10.1016/j.knosys.2015.01.006[LinkOut]

CHEN G, SUN J, MAO W T, et al. Limit cycle oscillation control for transonic aeroelastic systems based on support vector machine reduced order model[J]. Transactions of the Japan Society for Aeronautical and Space Sciences, 2013, 56(1): 8-14. DOI:10.2322/tjsass.56.8[LinkOut]

WANG Q, QIAN W Q, HE K F. Unsteady aerodynamic modeling at high angles of attack using support vector machines[J]. Chinese Journal of Aeronautics, 2015, 28(3): 659-668. DOI:10.1016/j.cja.2015.03.010[LinkOut]

CHEN S L, GAO Z H, ZHU X Q, et al. Unsteady aerodynamic modeling of unstable dynamic process[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(8): 232-243. (in Chinese)[万方]

ANDERSON J D Jr. Hypersonic and high-temperature gas dynamics[M]. Second edition, Reston, VA: AIAA, 2006. doi: 10.2514/4.861956

CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297. DOI: 10.1023/A:1022627411411[LinkOut]

ZHU L Y. Study on turbulence modeling with machine learning methods in aeronautical engineering[D]. Xi'an: Northwestern Polytechnical University, 2021 (in Chinese).

YE K. Research on the critical aerothermoelastic problems for air-breathing hypersonic vehicle[D]. Xi'an: Northwestern Polytechnical University, 2018 (in Chinese).

WIETING A R, HOLDEN M S. Experimental shock-wave interference heating on a cylinder at Mach 6 and 8[J]. AIAA Journal, 1989, 27(11): 1557-1565. DOI:10.2514/3.10301[LinkOut]

LI S X. Typical profile hypersonic flow characteristics[M]. Beijing: National Defense Industry Press, 2007 (in Chinese).

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