Optimization of sampling characteristics based on light field temperature measurement

High temperature combustion phenomenon is widespread in aerospace and industrial production. The development of accurate and efficient combustion temperature measurement technology is of great significance for monitoring the operation status of equipment. Tomography is a non-contact optical combustion diagnostic technique that uses an optical field camera to detect the flame's own outgoing radiation field. It has promising applications in three-dimensional parametric field reconstruction of flames. Aiming at the problems of slow imaging efficiency caused by dense sampling and information redundancy in the process of light field camera information acquisition, based on the forward problem model established by light field tomography and apparent light method and the inverse problem model established by Landweber algorithm, the genetic algorithm is used to optimize the light field sampling characteristics and analyze the influence of noise on the temperature field reconstruction results. The results show that the optimized feature sampling points can obtain three-dimensional temperature imaging results with high accuracy and strong noise immunity, which meets the accuracy requirements offlame temperature measurement and imaging.