Intelligent Optimization Strategy Based on Statistical Machine Learning for Spacecraft Thermal Design

doi:10.1109/access.2020.3036548

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	Intelligent Optimization Strategy Based on Statistical Machine Learning for Spacecraft Thermal Design
	Y. Xiong,L. Guo,D. F. Tian,Y. Zhang and C. L. Liu
	2020
发表期刊	Ieee Access
ISSN	2169-3536
卷号	8 页码:204268-204282
摘要	The thermal design of spacecraft becomes increasingly complicated as various advanced technologies are continuously introduced to the spacecraft. Determining and optimizing the uncertainties of a spacecraft thermal control system through global sensitivity analysis has long been an essential task for thermal engineers. It is a difficult task that relies heavily on engineering experience and is a time-intensive, trial-and-error endeavor that may not even lead to global optimization. Hence, an intelligent optimization strategy based on statistical machine learning for spacecraft thermal design, called IOSML, is proposed. An intelligent batch processing system (IBPS) based on MATLAB, Python, and NX/TMG real-time data interaction is designed. The IBPS uses a surrogate model to reduce the computational cost of global sensitivity analysis while using a detailed thermal mathematical model to maintain accuracy. We combine a Bayesian inference framework with a neural network surrogate spacecraft-thermophysical model that is 100x faster than numerical solvers. This article first reports on a density-based global sensitivity analysis that evaluates the effect of design parameters on the temperature difference between the complementary metal-oxide-semiconductor and cold screen of the Lehman Alpha Solar Space Telescope detector. From 42 design parameters, the most sensitive four are selected for optimization, and the temperature difference and the boundary temperature are used as the objective function. Adopting IOSML, under no supervision, four design parameters are optimized through the IBPS, and the effectiveness of the algorithm is verified by comparison with traditional methods. Additionally, IOSML is versatile and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.
DOI	10.1109/access.2020.3036548
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收录类别	SCI
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/64623
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y. Xiong,L. Guo,D. F. Tian,Y. Zhang and C. L. Liu. Intelligent Optimization Strategy Based on Statistical Machine Learning for Spacecraft Thermal Design[J]. Ieee Access,2020,8:204268-204282.
APA	Y. Xiong,L. Guo,D. F. Tian,Y. Zhang and C. L. Liu.(2020).Intelligent Optimization Strategy Based on Statistical Machine Learning for Spacecraft Thermal Design.Ieee Access,8,204268-204282.
MLA	Y. Xiong,L. Guo,D. F. Tian,Y. Zhang and C. L. Liu."Intelligent Optimization Strategy Based on Statistical Machine Learning for Spacecraft Thermal Design".Ieee Access 8(2020):204268-204282.

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