Reconstructing the self-luminous image of a flame in a supersonic combustor based on residual network reconstruction algorithm

被引:20
作者
Deng, Xue [1 ,2 ]
Guo, Mingming [1 ,2 ]
Tian, Ye [1 ,2 ]
Li, Linjing [1 ,2 ]
Le, Jialing [2 ]
Zhang, Hua [1 ]
Zhong, Fuyu [2 ]
机构
[1] Southwest Univ Sci & Technol, Mianyang 621000, Peoples R China
[2] China Aerodynam Res & Dev Ctr, Mianyang 621000, Peoples R China
关键词
FLOWFIELD RECONSTRUCTION; SCRAMJET COMBUSTOR; AIR;
D O I
10.1063/5.0140443
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
The reconstruction of the self-luminous image of a flame through deep learning can inform research on the characteristics of combustion of a scramjet. In this study, the authors propose a residual network model based on the channel and spatial attention mechanisms to reconstruct the self-luminous image of a flame from schlieren images of the flow field of a scramjet. We compare the reconstruction-related performance of single-path and dual-path models under different conditions. The channel and spatial attention mechanisms enable the model to focus on important feature-related information, and the residual connection prevents gradient disappearance to improve the capability of the model for generalization. The proposed method was tested through a supersonic combustion experiment in a ground wind tunnel under different equivalence ratios, and data on the flow field of the combustion chamber and the evolution of the flame were recorded as a dataset. A number of experiments as well as subjective and objective analyses were subsequently carried out on this dataset. The results show that the effect of reconstruction is consistent with the original image of the flame, and the geometric characteristics of the flame are accurately reconstructed.
引用
收藏
页数:17
相关论文
共 40 条
[1]   Closed-Loop Turbulence Control: Progress and Challenges [J].
Brunton, Steven L. ;
Noack, Bernd R. .
APPLIED MECHANICS REVIEWS, 2015, 67 (05)
[2]  
CARLSON, 1982, 82315 AIAA
[3]   Intelligent reconstruction of the flow field in a supersonic combustor based on deep learning [J].
Chen, Hao ;
Guo, Mingming ;
Tian, Ye ;
Le, Jialing ;
Zhang, Hua ;
Zhong, Fuyu .
PHYSICS OF FLUIDS, 2022, 34 (03)
[4]   Dual-path flow field reconstruction for a scramjet combustor based on deep learning [J].
Deng, Xue ;
Guo, Mingming ;
Chen, Hao ;
Tian, Ye ;
Le, Jialing ;
Zhang, Hua .
PHYSICS OF FLUIDS, 2022, 34 (09)
[5]   Turbulence Modeling in the Age of Data [J].
Duraisamy, Karthik ;
Iaccarino, Gianluca ;
Xiao, Heng .
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51, 2019, 51 :357-377
[6]   A deep learning approach for the transonic flow field predictions around airfoils [J].
Duru, Cihat ;
Alemdar, Hande ;
Baran, Ozgur Ugras .
COMPUTERS & FLUIDS, 2022, 236
[7]   Deep learning model to assist multiphysics conjugate problems [J].
El Haber, George ;
Viquerat, Jonathan ;
Larcher, Aurelien ;
Ryckelynck, David ;
Alves, Jose ;
Patil, Aakash ;
Hachem, Elie .
PHYSICS OF FLUIDS, 2022, 34 (01)
[8]   Super-resolution reconstruction of turbulent flows with machine learning [J].
Fukami, Kai ;
Fukagata, Koji ;
Taira, Kunihiko .
JOURNAL OF FLUID MECHANICS, 2019, 870 :106-120
[9]   Super-resolution reconstruction of flow field of hydrogen-fueled scramjet under self-ignition conditions [J].
Guo, Mingming ;
Chen, Erda ;
Tian, Ye ;
Chen, Hao ;
Le, Jialing ;
Zhang, Hua ;
Zhong, Fuyu .
PHYSICS OF FLUIDS, 2022, 34 (06)
[10]   Experimental investigations on mixing characteristics in the critical regime of a low-area ratio supersonic ejector [J].
Gupta, Pradeep ;
Rao, Srisha M. V. ;
Kumar, Pramod .
PHYSICS OF FLUIDS, 2019, 31 (02)