Investigation on thermal model updating of Alpha Magnetic Spectrometer in orbit based on Kriging meta-modeling

The purpose of the update of the space instrument thermal model is to make adjustments to the model parameters to maintain the consistency between the temperature simulation consequences and the experimental data, which is essential for thermal control design. This study proposed an approach of combining Kriging meta modeling and Genetic Algorithm (GA) instead of direct iteration to update the Alpha Magnetic Spectrometer (AMS) thermal model, which significantly boosts the updating efficiency. Firstly, the importance ranking of the thermal model parameters affecting its temperature response is determined by the parameter sensitivity analysis. The Kriging surrogate model of the AMS simulation temperature is then established by training samples from sensitive parameters using Latin Hypercube Sampling (LHS) method, and its reliability is demonstrated by the maximum error of 0.86 C in contrast to thermal model results at other specific model parameter values. Based on above information, a set of optimal parameters are eventually generated to update the original AMS thermal model by GA with the improvement of the temperature prediction accuracy up to 13.96%. The current work provides an efficient and accurate approach for thermal model optimization and thermal control design of the space-borne instruments.