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弹箭与制导学报 >

2025 , Vol. 45 >Issue 6: 1113 - 1119

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.06.020

学术文章

基于高斯过程回归的超声速燃烧室构型优化

  • 张皓 ,
  • 邓恒 ,
  • 李嘉航 ,
  • 颜密 , * ,
  • 妙远洋 ,
  • 宋一啸
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  • 西安现代控制技术研究所,陆空基信息感知与控制全国重点实验室,陕西 西安 710065
颜密(1988—),女,高级工程师。E-mail:。

张皓(1993—),男,高级工程师。E-mail:。

收稿日期: 2024-11-04

  网络出版日期: 2026-01-24

基金资助

由陆空基信息感知与控制全国重点实验室资助

收起

Optimization of Supersonic Combustion Chamber Configuration Based on Gaussian Process Regression

  • ZHANG Hao ,
  • DENG Heng ,
  • LI Jiahang ,
  • YAN Mi , * ,
  • MIAO Yuanyang ,
  • SONG Yixiao
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  • National Key Laboratory of Land and Air Based Information Perception and Control,Xi’an Modern Control Technology Research Institute,Xi’an 710065,Shaanxi,China

Received date: 2024-11-04

  Online published: 2026-01-24

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摘要

本文采用遗传算法对基于数值仿真与高斯过程回归建立的代理模型开展关于凹腔底部前向燃料水平喷注的固体火箭超燃冲压发动机燃烧室几何构型的最优化研究。建立了一个包含RANs方程,SST k-ω湍流方程,H2/CO/CH4四步简化气相反应动力模型,碳颗粒三步简化固相表面化学反应动力模型,离散相模型的超音速流动燃烧数值仿真模型。通过该数值仿真模型,对一个考虑了5个设计变量的凹腔底部喷注燃料的固体火箭超燃冲压发动机燃烧室进行了432次数值仿真计算以获得数据库。将该数据库的90%划分为训练集,剩余10%划分为验证集。采用平方指数核函数-高斯过程回归模型对训练集进行训练以获取代理模型,并使用三种度量方法考核代理模型的精度。最后,采用遗传算法以总温温升系数最大为目标对燃烧室构型开展最优化研究。研究结果表明,最优化后的案例较基础案例总温温升系数提升41.2%,较总温温升系数最低的案例提升307%。

关键词： 高斯过程回归; 遗传算法; 固体火箭超燃冲压发动机; 最优化

本文引用格式

张皓 , 邓恒 , 李嘉航 , 颜密 , 妙远洋 , 宋一啸 . 基于高斯过程回归的超声速燃烧室构型优化[J]. 弹箭与制导学报, 2025 , 45(6) : 1113 -1119 . DOI: 10.15892/j.cnki.djzdxb.2025.06.020

Abstract

In this paper,genetic algorithm is used to optimize the combustion chamber geometry of a solid rocket scramjet based on a surrogate model based on numerical simulation and Gaussian process regression.A numerical simulation model of supersonic flow combustion including RANs equation,SST k-ω turbulence equation,H2/CO/CH4 four-step simplified gas phase reaction dynamic model,carbon particle three-step simplified solid phase surface chemical reaction dynamic model and discrete phase model was established.Through the numerical simulation model,432 numerical simulation calculations were performed on a solid rocket scramjet combustion chamber with fuel injection at the bottom of the cavity considering 5 design variables to obtain a database.90% of the database is divided into training sets and the remaining 10% into validation sets.The quadratic exponential kernel function and Gaussian process regression model are used to train the training set to obtain the surrogate model,and three metric methods are used to check the accuracy of the surrogate model.Finally,genetic algorithm is used to optimize the combustion chamber configuration with the aim of maximizing the total temperature rise coefficient.The results show that the total temperature rise coefficient of the optimized case is 41.2% higher than that of the basic case,and 307% higher than that of the case with the lowest total temperature rise coefficient.

Key words： gaussian process regression; genetic algorithm; solid rocket scramjet engine; optimization

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参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
Preller D, Smart M K. Reusable launch of small satellites using scramjets[J]. J Spacecr Rocket, 2017,54:1317-1329.

[2]
Forbes-Spyratos S O, Kearney M P, Smart M K, et al. Trajectory design of a rocket-scramjet-rocket multistage launch system[J]. J Spacecr Rocket, 2019,56:53-67.

[3]
ZHAO X, XIA Z X, Ma LI K, et al. Research progress on solid-fueled Scramjet[J]. Chinese Journal of Aeronautics, 2022,35:398-415.

[4]
LI C L, XIA Z X, MA L K, et al. Experimental and numerical study of solid rocket scramjet combustor equipped with combined cavity and strut device[J]. Acta Astronautica, 2019,162:145-154.

[5]
Witt M A. Investigation into the feasibility of using solid fuel ramjets for high supersonic/low hypersonic tactical missiles[D].Monterey, CA:Deft. of Astronautical Engineering,Naval Postgraduate School,1989.

[6]
Angus W J. An investigation into the performance characteristics of a solid fuel scramjet propulsion device[D].Monterey, CA:Deft. of Astronautical Engineering,Naval Postgraduate School,1991.

[7]
Ben-Yakar A, Natan B, Gany A. Investigation of a solid fuel scramjet combustor[J]. J Propuls Power, 1998,14:447-455.

[8]
WANG L, WU Z, CHI H. Numerical and experimental study on the solid-fuel scramjet combustor[J]. J Propuls Power, 2014,31:685-693.

[9]
Smirnov N N. Heat and mass transfer in a multi-component chemically reactive gas above a liquid fuel layer[J]. Int J Heat Mass Transf, 1985,28:929-938.

[10]
ZHANG H, WANG N, WU Z, et al. A new model of regression rate for solid fuel scramjet[J]. International Journal of Heat and Mass Transfer, 2019,144:118645.

[11]
LV Z, XIA Z, LIU B, et al. Experimental and numerical investigation of a solid-fuel rocket scramjet combustor[J]. J Propuls Power, 2016,32:273-278.

[12]
Betelin V B, Kushnirenko A G, Smirnov N N, et al. Numerical investigations of hybrid rocket engines[J]. Acta Astronaut, 2018,144:363-370.

[13]
Gariani G, Maggi F, Galfetti L. Numerical simulation of HTPB combustion in a 2D hybrid slab combustor[J]. Acta Astronaut, 2011,69:289-296.

[14]
LI J, DU X, Martins J R. Machine learning in aerodynamic shape optimization[J]. Prog Aerosp Sci, 2022.

[15]
Queipo N V, Haftka R T, Shyy W, et al. Surrogate-based analysis and optimization[J]. Prog Aerosp Sci, 2005, 41(1):1-28.

DOI

[16]
MA Y, GUO M M, ZHANG Y, et al. Dynamic multi-objective optimization of scramjet inlet based on small-sample Kriging model[J]. Physics of Fluids, 2023, 35(9):095136.

DOI

[17]
Brahmachary Shuvayan, Fujio Chihiro, Ogawa Hideaki. Multi-point design optimization of a high-performance intake for scramjet-powered ascent flight[J]. Aerosapce Science and Technology, 2020,107:106362.

[18]
Fujio Chihiro, Ogawa Hideaki. Sensitivity analysis for knowledge discovery in scramjet intake design optimization using deep-learning flowfield prediction[J]. Aerosace Science and Technology, 2024,150:109183.

[19]
Ispir Ali C, Zdybal Kamila, Saracoglu Bayindir H, et al. Reduced-order modeling of supersonic fuel-air mixing in a multi-strut injection scramjet engine using machine learning techniques[J]. Acta Asronautica, 2023,202:564-584.

[20]
Ma L. Computational modeling of turbulent spray combustion[D]. Delft: Department of Process and Energy,Delft University of Technology, 2016.

[21]
Menter F R. Review of the shear-stress transport turbulence model experience from an industrial perspective[J]. Int J Comput Fluid Dyn, 2009,23:306-3016.

[22]
PEI X, WU Z, WEI Z, et al. Numerical investigation on internal regressing shapes of solid-fuel scramjet combustor[J]. J Propuls Power, 2013,29:1041-1051.

[23]
Kummitha O R, Suneetha L, Pandey K M, et al. Numerical analysis of scramjet combustor with innovative strut and fuel injection techniques[J]. Int J Hydrogen Energy, 2017,42:10524-10535.

[24]
Dharavath M, Manna P, Chakraborty D. Numerical exploration of mixing and combustion in ethylene fueled scramjet combustor[J]. Acta Astronaut, 2015,117:305-318.

[25]
Jones W P, Lindstedt R P. Global reaction schemes for hydrocarbon combustion[J]. Combust Flame, 1988,73:233-249.

[26]
WANG F, LI P, MI J, et al. A refined global reaction mechanism for modeling coal combustion under moderate or intense low-oxygen dilution condition[J]. Energy, 2018,157:764-777.

[27]
ZHANG J, Prationo W, ZHANG L, et al. Computational fluid dynamics modeling on the air-firing and oxy-fuel combustion of dried Victorian brown coal[J]. Energy Fuels, 2013,27:4258-4269.

[28]
CHEN L, YONG S Z, Ghoniem A F. Oxy-fuel combustion of pulverized coal:Characterization,fundamentals,stabilization and CFD modeling[J]. Progress in Energy and Combustion Science, 2012,38:156-214.

[29]
Toporov D, Bocian P, Heil P, et al. Detailed investigation of a pulverized fuel swirl flame in CO2/O2 atmosphere[J]. Combustion and Flame, 2008,155:605-618.

[30]
Williams C K I, Rasmussen C E. Gaussian processes for machine learning[M]. Cambridge,MA: MIT press, 2006.

[31]
Goldberg D E. Genetic Algorithms in Search, Optimization & Machine Learning[M]. Addison-Wesley,1989.

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