Temperature Tomography for Combustion Field Based on Hierarchical Vision Transformer and Multi-scale Features Merging

SI Jingjing; WANG Xiaoli; CHENG Yinbo; LIU Chang

doi:10.11999/JEIT221034

Volume 45 Issue 10

Oct. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(10): 3511-3519

SI Jingjing, WANG Xiaoli, CHENG Yinbo, LIU Chang. Temperature Tomography for Combustion Field Based on Hierarchical Vision Transformer and Multi-scale Features Merging[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3511-3519. doi: 10.11999/JEIT221034

Citation:

SI Jingjing, WANG Xiaoli, CHENG Yinbo, LIU Chang. Temperature Tomography for Combustion Field Based on Hierarchical Vision Transformer and Multi-scale Features Merging[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3511-3519. doi: 10.11999/JEIT221034

Citation:

SI Jingjing, WANG Xiaoli, CHENG Yinbo, LIU Chang. Temperature Tomography for Combustion Field Based on Hierarchical Vision Transformer and Multi-scale Features Merging[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3511-3519. doi: 10.11999/JEIT221034

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Temperature Tomography for Combustion Field Based on Hierarchical Vision Transformer and Multi-scale Features Merging

doi: 10.11999/JEIT221034

SI Jingjing^{1, 4},
WANG Xiaoli¹,
CHENG Yinbo^{2
,
,},
LIU Chang³

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
Ocean College, Hebei Agricultural University, Qinhuangdao 066003, China
3.
School of Engineering, The University of Edinburgh, Edinburgh EH93JL, UK
4.
Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066004, China

Funds: The Natural Science Foundation of Hebei Province (F2021203027), The Cultivation Project for Basic Research and Innovation of Yanshan University (2021LGZD011), Hebei Key Laboratory Project (202250701010046)

Received Date: 2022-08-08
Rev Recd Date: 2022-10-24

Available Online: 2022-10-26

Publish Date: 2023-10-31

Abstract

Abstract

Tunable Diode Laser Absorption Tomography (TDLAT) is an important non-intrusive combustion diagnostic technology, which can be used to reconstruct two-dimensional cross-sectional distributions of flow-field parameters such as gas temperature and concentration in the combustion field. In this paper, Vision Transformer (ViT) and multi-scale features merging are introduced into TDLAT to study the nonlinear mapping between a limited number of measurement data and the temperature distribution in the entire tomographic filed. Temperature tomography network (HVTMFnet) is proposed based on the hierarchical Vision Transformer (ViT) and Multi-scale Features merging. By extracting and merging the local and global correlation characteristics of TDLAT measurement data, HVTMFnet reconstructs the hierarchical temperature distribution in the entire tomographic field. Both simulations and lab-scale experiments with TDLAT system show that HVTMFnet retrieves better-quality temperature images than existing temperature tomography schemes based on Convolutional Neural Network (CNN) and residual network. In comparison to the temperature tomography scheme based on CNN, HVTMFnet reduces the reconstruction error by 49.2%～72.1%.
- Tunable Diode Laser Absorption Spectroscopy (TDLAS),
- Tomography,
- Temperature reconstruction,
- Vision Transformer (ViT)

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References(15)

References

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