Thermal design parameters analysis and model updating using Kriging model for space instruments

Abstract

In this study, we performed a thermal simulation analysis of a space instrument, a solar spectrometer. A thermal model updating method was used to introduce the Kriging model as the surrogated model into optimizing thermal design parameters instead of directly iterating the finite element analysis. The sensitivity analysis method was used to eliminate the insensitive parameters, thus determining the influence area of modeling parameters and saving processing time. The valid parameters were then used in Latin Hypercube Sampling (LHS) to generate training samples. Eight Kriging models were constructed by the training samples, and a Genetic Algorithm (GA) was used to find the optimal set of parameters, under which the temperature values at certain positions of the model were closest to the results of the heat balance experiment, thus updating the thermal model. The proposed method was successfully performed on the thermal design of a space instrument. Using this model, temperatures of specialized positions predicted by the updated model were more precise than the initial ones with the RMSE of temperature deviation of 0.88 °C. The surrogate model updating technology based on Kriging is rapid and efficient for the iterative thermal design of aerospace products.