基于级联视觉Transformer与多尺度特征融合的燃烧场温度层析成像

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

可调谐二极管激光吸收光谱层析成像(TDLAT)是一种重要的光学非侵入式燃烧诊断技术,可实现燃烧场2维横截面气体温度和浓度等流场参数分布的重建。该文将视觉Transformer(ViT)与多尺度特征融合引入TDLAT领域,研究有限数量测量数据与整个测量空间温度分布的非线性映射,提出基于级联ViT与多尺度特征融合的燃烧场温度层析成像网络(HVTMFnet)。该网络提取并融合TDLAT测量数据的局部-全局相关特征,实现整个测量空间的层次化温度分布重建。仿真实验与实际TDLAT系统实验均表明,HVTMFnet重建图像的质量优于现有的基于卷积神经网络(CNN)和基于残差网络的温度层析成像方案。与基于CNN的温度层析成像方案相比,HVTMFnet的重建误差能够降低49.2%~72.1%。

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%.