Abstract
The thermal design parameters of space telescopes are mainly optimized through traversal and iterative attempts. These optimization techniques are time consuming, rely heavily on the experience of the engineer, bear a large computational workload, and have difficulty in achieving optimal outcomes. In this paper, we propose a design method (called SMPO) based on an improved back-propagation neural network (called GAALBP) that builds a surrogate model and uses a genetic algorithm to optimize the model parameters. The surrogate model of a space telescope that measures the atmospheric density is established using GAALBP and then compared with surrogate models established using a traditional BP neural network and radial-basis-function neural network. The results show that the regression rate of the surrogate model based on the GAALBP reaches 99.99%, a mean square error of less than 2 × 10−6, and a maximum absolute error of less than 4 × 10−3. The thermal design parameters of the surrogate model are optimized using a genetic algorithm, and the optimization results are verified in a finite element simulation. Compared with the design results of the manually determined thermal design parameters, the maximum temperature of the CMOS is reduced by 5.33 °C, the minimum temperature is increased by 0.39 °C, and the temperature fluctuation is reduced by a factor of 4. Additionally, SMPO displays versatility and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.
Highlights
Space telescopes are developing toward deep space exploration and maneuver to change orbit, with increasing demands on imaging quality, they experience changing and complex thermal environments [1,2]
The development of methods that allow the rapid optimization of the thermal design parameters of telescopes has become an important issue [4], and techniques of parameter optimization have received much attention in recent years
On the basis of the above analysis, this paper proposes a design method that uses an improved BP neural network to establish a telescope surrogate model and optimizes the model parameters using a genetic algorithm (GA) to optimize the thermal design of a telescope
Summary
Space telescopes are developing toward deep space exploration and maneuver to change orbit, with increasing demands on imaging quality, they experience changing and complex thermal environments [1,2]. These optimization algorithms are combined with traditional physical models to conduct finite element iterative calculations after selecting parameter combinations, which is a time-consuming process of solving partial differential equations that greatly reduces the speed of parameter optimization.
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