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为提高星载电子器件热分析的模拟分辨率和精度以及被动热控装置的控温效果，本文建立系统多尺度模型获得不同尺度下卫星内部电子器件的温度场和热流信息。结果表明：系统多尺度模型在系统级尺度模拟精度与实际模型相对误差小于8%，并且可消耗较少的计算资源获得器件级尺度芯片微小结构的热信息。系统级模型可从宏观尺度评估星载被动热控材料的控温隔热性能，采用复合相变隔热材料可将载荷舱室温度波动幅值降至2.43 K，相比平台舱室温度波动幅值降低约69.43%，通过复合相变隔热材料隔热后的温度波动信号呈现向高频域部分转移的特征。通过多元回归分析选定需要进行重点隔热控温的舱室后，采用器件级简化模型得到不同热控装置布局下的温度场信息形成训练数据集，采用神经网络遗传算法在器件尺度预测被动热控装置的最佳安装位置并得到减小器件最高温度波动的热控布局方案，最高温度波动降低2.74 K。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 was established to obtain the temperature field and heat flux of electronic devices inside the satellite at different scales as schematic in the below figure. The temperature fluctuation mechanism inside the satellite was analyzed at different physical scales. The thermal analysis resolution of spaceborne electronic equipment was improved, and a method to reduce the power fluctuation of spaceborne equipment was proposed based on the results of system multi-scale thermal analysis.
The results show that the system multi-scale model presents an accuracy deviation below 8% from the actual model. 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 of device-level chip microstructures with less computational cost. The system-level model can evaluate the temperature control and insulation performance of passive thermal control materials from a macroscale. The temperature fluctuation amplitude of the platform compartment was 7.95 K, while the temperature fluctuation amplitude of the load compartment was reduced to 2.43 K after the temperature control of the composite phase change insulation material, which was 69.43% lower than that of the platform compartment. Compared with traditional vacuum insulation panels, the composite phase change materials are more superior in controlling the temperature of the chamber and suppressing temperature fluctuations. The temperature fluctuation signal after insulation 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 was employed to obtain temperature field under different thermal control device layouts as a training dataset. A neural network genetic algorithm was used to predict the optimal installation position of passive thermal control devices at the device scale and a thermal control layout scheme was obtained that reduces the maximum temperature fluctuation of the devices 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 optimization.-
Keywords:
- spaceborne electronic device /
- system multi-scale /
- temperature control /
- neural network genetic algorithm
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