基于LSTM的勘察器外壁温度反演方法研究

Research on inversion method of external temperature of penetrator based on LSTM

对于星壤物质物化特性的反演是深空探测中最重要的一环,星壤的热导率、热容参数等热特性是研究星壤组成的科学依据,而温度测量是基于侵彻式的星壤原位探测的重要参数。针对月壤勘察器侵彻过程的外表面温度无法直接测量的问题,开展了基于LSTM神经网络算法的勘察器外表面温度反演方法的研究。借助ANSYS/LS-DYNA有限元软件实现侵彻过程的仿真模拟以获取多组勘察器弹头部内外表面温度数据,依据有限差分法离散热传导方程选取数据,采用长短期记忆神经网络来建立反演模型。通过模拟实验进行分析,该方法反演所得曲线和实验曲线相比均方根误差为12.9℃,最大相对误差不超过10%。实验结果表明本文所研究的方法可以实现勘察器外表面温度的反演。

The inversion of physical and chemical properties of satellite soil is the most important part of deep space exploration,and thermal properties such as thermal conductivity and heat capacity parameters are the scientific basis for studying the composition of satellite soil,and temperature measurement is an important parameter for in-situ detection of satellite soil based on penetration.In this paper,the surface temperature inversion method of lunar soil probe based on LSTM neural network algorithm is studied to solve the problem that the surface temperature of lunar soil probe can not be measured directly.The penetration process was simulated by ANSYS/LS-DYNA finite element software to obtain the temperature data of multiple groups of reconnaissance warhead.The data were selected according to the finite difference method of discrete heat conduction equation,and the inversion model was established by using the long and short term memory neural network.The root-mean-square error of the inversion curve is 12.9℃and the maximum relative error is less than 10%compared with the experimental curve.The experimental results show that the method proposed in this paper can realize the inversion of the outer surface temperature of the probe.