Study On Analysis And Design Method Of Light-Weight Relector

This paper purposed a method based on Neural Network aims to build a math model for pointing mirror of large space-based telescope, which can reduce weight of the telescope and smaller mirror deformation caused by gravity release effectively. In the meantime, it can also improve stiffness of structure and reduce thermal strain caused by on orbit temperature variation effectively. The model describes the relationship between the structure of pointing mirror of large space-based telescope and corresponding deformation. To optimize the structure of pointing mirror lightweight, we take the deformation of mirror and its optical performance into consideration. To apply the structures parameters and its corresponding deformations to Neural Network training,we use the combination samples of different mirror lightweight structure parameters and corresponding deformation which caused by gravity release and thermal condition. Finally, by taking advantage of the Neural Network model to optimize the pointing mirror lightweight of 1-meter rectangle space-based telescope,which can make the RMS≤1/40λ(λ=632.8nm), PV≤1/15λ and areal density under 15kg/m~2. And the theoretical optimization model is further verified through the finite element simulation analysis.This method combines existing results and numerical simulation to establish numerical model based on Neural Network method. Research results can be applied to same processes of designing, analyzing, and processing of large space-based telescope pointing mirror directly.