基于BP神经网络的星外设备温度预测
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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.
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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[3]Rose T S,Janson S W,Lalumondiere S,et al.LEO to Ground Optical Communications From a Small Satellite Platform[C]//SPIE LASE.International Society for Optics and Photonics,2015:935401-935401
[4]Wang C,Yang W,Feng L,et al.Thermal Design and Simulation of an Attitude-varied Space Camera[C]//Applied Optics and Photonics China.2015:96780U
[5]Meng H,Sun L,Zhang C,et al.Thermal Design and Flight Validation for High Precision Camera[C]//Applied Optics and Photonics China.International Society for Optics and Photonics,2015:15490-155011
[6]徐向华,程雪涛,梁新刚.圆形太阳同步轨道卫星的空间热环境分析[J].宇航学报,2012,33(3):399-404XU Xianghua,CHENG Xuetao,LIANG Xingang.Analysis on Space Thermal Environment for Circular SunSynchronous Orbit[J].Journal of Astronautics,2012,33(3):399-404
[7]马伟,宣益民,韩玉阁.临近空间热环境分析及低速飞行器的热设计方法[J].宇航学报,2010,31(5):1272-1277MA Wei,XUAN Yimin,HAN Yuge.Principle of Stratospheric Vehicles[J].Journal of Astronautics,2010,31(5):1272-1277
[8]赵振明,鲁盼,宋欣阳,“高分二号”卫星相机热控系统的设计与验证[J].航天返回与遥感,2015,36(4):34-40ZHAO Zhenming,LU Pan,SONG Xinyang.Thermal Design and Test for High Resolution Space Camera on GF-2Satellite[J].Spacecraft Recovery&Remote Sensing,2015,36(4):34-40
[9]刘百麟,董艺,魏巍.卫星平台机热一体化设计探讨[J].航天器工程,2016,25(2):25-31LIU Bailin,DONG Yi,WEI Wei.Discussion on Mechanical and Thermal Integrated Design of Satellite Platform[J].Spacecraft Engineering,2016,25(2):25-31
[10]孙大强,谢龙,翟载腾,等.百叶窗热性能优化设计研究[J].上海航天,2016,33(1):95-100SUN Daqiang,XIE Long,ZHAI Zaiteng etl.Study on Optimization Design of Thermal Louver Performance[J].Aerospace Shanghai,2016,33(1):95-100
[11]张桂兰,周佐新.中星22号通信卫星热设计[J].航天器工程,2002(1):24-27ZHANG Guilan,ZHOU Zuoxin.Thermal Design of China22 Communication Satellite[J].Spacecraft Engineering,2002(1):24-27
[12]李萍,曾令可,税安泽,等.基于MATLAB的BP神经网络预测系统的设计[J].计算机应用与软件,2008,25(4):149-150LI Ping,ZENG Lingke,SHUI Anze etl.Design of Forecast System of Back Propagation Neural Network Based on MATLAB[J].Computer Applications and Software,2008,25(4):149-150
[13]李宇峰,裴旭东,黄聪明.BP神经网络实际应用中的若干问题[J].兵工自动化,1998(1):1-4LI Yufeng,PEI Xudong,HUANG Congming.Some Question of BP Neural Network In the Application[J].Ordnance Industry Automation,1998(1):1-4