光学学报, 2020, 40 (23): 2312003, 网络出版: 2020-11-23   

基于机器学习对火焰温度场和CO2浓度场的同步重建 下载: 1289次

Machine-Learning-Based Reconstruction of Flame Temperature and CO2 Concentration Fields
作者单位
1 上海交通大学中英国际低碳学院, 上海 201306
2 上海交通大学机械与动力工程学院, 上海 200240
摘要
基于可调谐二极管激光吸收光谱法(TDLAS)和传统的反演重建算法对轴对称火焰的二维温度场和CO2浓度场的同步重建通常需要进行空间轴向和径向的多视线扫描式测量,测量系统相对复杂,反演重建效率不佳。本文基于4.2 μm 中红外TDLAS激光测量系统,针对轴对称层流扩散火焰,建立了能够同步反演火焰温度场和CO2浓度场的机器学习模型。与传统的反演重建方法相比,采用机器学习的反演模型只需要对火焰中心轴向进行扫描式测量就能同步、高效地重建轴对称层流扩散火焰的二维温度场和CO2浓度场,在相同的硬件条件下需要更少的实验测量数据,能够简化实验测量的复杂度并提高反演重建的效果。
Abstract
Reconstruction of two-dimensional temperature and CO2 concentration fields based on the tunable diode laser absorption spectroscopy (TDLAS) and traditional reconstruction algorithm requires multiple line-of-sight measurements in both axial and radial directions for axisymmetric flames. The experimental system is usually complicated, and the reconstruction efficiency is relatively low. Herein, a machine-learning-based reconstruction model is developed and used to simultaneously retrieve the two-dimensional temperature and CO2 concentration fields from 4.2-μm mid-infrared TDLAS laser absorption measurements for axisymmetric laminar diffusion flames. Compared with the traditional inversion reconstruction method, the machine-learning-based inversion model only needs to scan the central axis of the flame to simultaneously and efficiently reconstruct the two-dimensional temperature and CO2 concentration field of an axisymmetric laminar diffusion flame, and the model requires less experimental measurements only in the axial direction, which considerably simplifies the measurement system and improves the reconstruction performance.

张倚成, 韩永康, 周亚, 任涛, 刘训臣. 基于机器学习对火焰温度场和CO2浓度场的同步重建[J]. 光学学报, 2020, 40(23): 2312003. Yicheng Zhang, Yongkang Han, Ya Zhou, Tao Ren, Xunchen Liu. Machine-Learning-Based Reconstruction of Flame Temperature and CO2 Concentration Fields[J]. Acta Optica Sinica, 2020, 40(23): 2312003.

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