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CO_(2)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4.2μm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CO_(2)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CO_(2)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。
Reconstruction of two-dimensional temperature and CO_(2) 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 CO_(2) 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 CO_(2) 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.
作者
张倚成
韩永康
周亚
任涛
刘训臣
Zhang Yicheng;Han Yongkang;Zhou Ya;Ren Tao;Liu Xunchen(China-UK Low Carbon College,Shanghai Jiao Tong University,Shanghai 201306,China;School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2020年第23期102-110,共9页
Acta Optica Sinica
基金
国家自然科学基金(91941301)
上海交通大学引进人才科研启动项目(WF20428005)。
关键词
光谱学
层流火焰
机器学习
温度
浓度
spectroscopy
laminar flame
machine learning
temperature
concentration
