Application and optimization of residual connection neural network in spacecraft thermal design

Abstract

In thermal analysis modeling, the finite element method (FEM) is commonly used; however, it incurs high computational costs and complicates the global optimization of thermal parameters. To address these challenges, developing simplified surrogate models is crucial for enhancing analysis efficiency. Yet, constructing such models demands exceptional predictive accuracy, making conventional parameter adjustment methods inadequate for design needs. This paper introduces a novel Residual connection Neural Network model, called Res-NN, designed to approximate the CMOS finite element model. By employing residual connections, the Res-NN model significantly reduces fitting errors between networks, achieving a predictive accuracy of 94.6 %, which is 6.6 % higher than that of comparable Multi-Layer Perceptron (MLP) models. Moreover, Res-NN is over 100 times faster than traditional FEM in prediction speed, effectively circumventing the difficulties associated with parameter adjustments. To optimize the temperature fluctuations of the CMOS model, we utilized the Res-NN model as an iterative object within an optimization algorithm. Through experimental comparisons, we identified the PSO algorithm as the most effective option. The PSO optimization results demonstrated a chip temperature difference of 0.045 °C, with a simulation error of only 0.0649 °C, meeting design specifications and achieving a 61.7 % reduction in temperature variance compared to traditional thermal design method. This validates the superiority of the entire optimization process.

Keywords

• Surrogate model
• Residual connected
• Neural network
• Optimization algorithms
• Spacecraft thermal design