Efficient Correlation Method for Satellite Thermal Analysis Model Using Multiple Linear Regression and Optimization Algorithms

As the thermal analysis model of satellites is used as an important indicator for thermal design, it must accurately simulate the thermal behaviour of actual satellites for precise thermal design. To increase the accuracy of the thermal analysis model, it must be correlated using the thermal balance test data for actual satellite models. To achieve this, we herein propose an efficient correlation method for satellite thermal analysis models using multiple linear regression techniques with quadratic terms and optimization algorithms. The proposed method reduces the amount of computation by choosing dominant parameters through sensitivity analysis and creating a multiple linear regression model that can replace the thermal analysis model in the subsequent optimization process. Subsequently, optimization algorithms are applied to the multiple linear regression model to perform the correlation of the thermal analysis model. In this study, the numerical validation of the proposed method was performed using numerical data from a reference thermal analysis model to verify the reliability and accuracy of the proposed method before it was applied to the correlation of the thermal analysis model using experimental data. The thermal analysis result of the reference thermal analysis model was set as the target value to correlate, and quantitative performance evaluation was performed for various combinations of optimization algorithms and design of experiments methods by comparing the estimated analysis parameters. The results of this study demonstrate that the proposed method can efficiently produce an accurate correlation model for thermal analysis.