Optimizing MRT data processing via comparative analysis of SA and PSO algorithms: a simulation and numerical study

Abstract

The unknown emissivity still poses a significant challenge in the data processing of Multispectral radiation thermometry (MRT). In order to achieve global optimal solution with fast convergence speed and strong robustness, a systematic comparative analysis of particle swarm optimization (PSO) and simulated annealing (SA) algorithms in the application in MRT is presented in this paper. The simulations of six hypothetical emissivity models were compared, and the results indicate that the PSO algorithm is superior to the SA algorithm in accuracy, efficiency and stability. The measured data of the surface temperature of rocket motor nozzle is simulated by the PSO algorithm, the maximum absolute error and the maximum relative error are 16.27 K and 0.65%, and the calculation time is less than 0.3 s. The superior performance of the PSO algorithm indicates that it can be well used in data processing for accurate temperature measurement in MRT, and the method proposed in this paper can be extended to other multispectral systems and applied to various industrial processes under high temperature conditions.