摘要
卫星舱外设备由于热容较小,所处的空间热环境复杂,热控设计难度较大。本文基于神经网络算法,建立了以星外设备热控设计参数为输入、设备温度为输出的BP神经网络模型。经过训练后的神经网络模型对样本数据的温度预测误差在1%左右,对新设计的热控参数预测误差在2%左右,表明所建立的BP神经预测温度模型精度高、稳定性强。通过所建立的神经网络模型对设备的热控参数进行了敏感性分析,分析结果表明支架的长度和支架安装面的传热系数对设备一轨内的最高温度影响更大。
The thermal design of small equipment out of satellite is a problem because of small heat capacity and complex space thermal environment.A BP neural network that thermal design parameters as input and temperature as output is build based on neural network algorithm.The prediction error of BP neural network after training is about 1%,and the prediction error for new thermal design input is about 2%.The result shows the BP artificial neural network is of higher precision and stability.Sensitivity analysis for thermal design parameters is carried through the neural network,which shows that the length of holder and the heat transfer coefficient of fitting surface of holder are more important for equipment's max temperature in the orbit.
引文
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