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Journal of Projectiles, Rockets, Missiles and Guidance >

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

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

Academic article

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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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

Cite this article

ZHANG Hao , DENG Heng , LI Jiahang , YAN Mi , MIAO Yuanyang , SONG Yixiao . Optimization of Supersonic Combustion Chamber Configuration Based on Gaussian Process Regression[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(6) : 1113 -1119 . DOI: 10.15892/j.cnki.djzdxb.2025.06.020

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References

Publishing order | Descend order by publishing year | Descend order by cited within
[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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