System multi-scale analysis of temperature control for electronic devices

To improve the simulation resolution and accuracy in thermal analysis of spaceborne electronic devices and the temperature control performance of passive thermal control devices, a system multi-scale model is established, thereby obtaining the temperature field and heat flux of electronic devices inside the satellite on different scales as illustrated in the below figure. The temperature fluctuation mechanism inside the satellite is analyzed on different physical scales. The thermal analysis resolution of spaceborne electronic equipment is improved, and a method to reduce the power fluctuation of spaceborne equipment is proposed based on the results of system multi-scale thermal analysis. The results indicate that the accuracy deviation between the multi-scale model of the system and the actual model is less than 9%. However, the system multi-scale model saves 99.67% of the mesh generation time, which greatly improves the computation efficiency. The system multi-scale model can capture the thermal information about device-level chip microstructures at a lower computational cost. The system-level model can evaluate the temperature control and insulation performance of passive thermal control materials on a macroscale. The temperature fluctuation amplitude of the platform compartment is 7.95 K, while the temperature fluctuation amplitude of the load compartment decreases to 2.43 K after the temperature of the composite phase change insulation material has been controlled, which is 69.43% lower than that of the platform compartment. Compared with traditional vacuum insulation panels, the composite phase change materials are very superior in controlling the temperature of the chamber and suppressing temperature fluctuations. The temperature fluctuation signal after being insulated by the composite phase change insulation materials shows a characteristic of shifting to the high-frequency domain. After selecting the cabins that require key insulation and temperature control through multiple regression analysis, a simplified model at device level is employed to obtain temperature fields under different thermal control device layouts as a training dataset. A neural network genetic algorithm is used to predict the optimal installation position of passive thermal control device on the device scale and a thermal control layout scheme is obtained, which reduces the maximum temperature fluctuation of the device by 2.74 K. If the temperature uniformity coefficient is taken as the optimization goal, the temperature of each device on PCB board can be reduced to 14.39% of the average temperature of all devices through optimizations. [GRAPHICS] .