Global sensitivity analysis based on bp neural network for thermal design parameters

doi:10.2514/1.T5955

CIOMP OpenIR

	Global sensitivity analysis based on bp neural network for thermal design parameters
	Y. Yang; L. Chen; Y. Xiong; S. Li and X. Meng
	2021
发表期刊	Journal of Thermophysics and Heat Transfer
ISSN	8878722
卷号	35 期号:1 页码:187-199
摘要	In order to obtain the thermal design parameters that have a great influence on the temperature T of the spectrometer frame, the sensitivity of the thermal design parameters of a balloon-borne spectrometer system was analyzed and calculated by the global sensitivity analysis (GSA) method based on the backpropagation neural network (BPNN) surrogate model. Firstly, the BPNN with 12 selected thermal design parameters as input and temperature T as output was well trained. Then, two kinds of variance-based GSA methods, the Sobol method and the extended Fourier amplitude sensitivity test (EFAST), were used to calculate the values and ranking results of sensitivity indices of 12 parameters based on the established BPNN. Moreover, the GSA results were verified based on the finite element model of the balloon-borne spectrometer system built by I-DEAS/TMG (software developed by Structural Dynamics Research Corporation for space thermal analysis), which indicates that the BPNN surrogate-model-based GSA is reliable. Finally, the sensitivity calculation accuracy and speed of two methods, the Spearman rank correlation coefficient formula and the GSA method based on BPNN, were compared, and the EFAST method based on the BPNN surrogate model has been proved to have obvious advantages in the reliability and speed of calculation results. Also, the GSA method based on a surrogate model like BPNN is of great significance in the thermal analysis of an optical remote sensor. 2020, AIAA International. All rights reserved.
DOI	10.2514/1.T5955
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收录类别	SCI ; EI
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文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65275
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. Yang,L. Chen,Y. Xiong,et al. Global sensitivity analysis based on bp neural network for thermal design parameters[J]. Journal of Thermophysics and Heat Transfer,2021,35(1):187-199.
APA	Y. Yang,L. Chen,Y. Xiong,&S. Li and X. Meng.(2021).Global sensitivity analysis based on bp neural network for thermal design parameters.Journal of Thermophysics and Heat Transfer,35(1),187-199.
MLA	Y. Yang,et al."Global sensitivity analysis based on bp neural network for thermal design parameters".Journal of Thermophysics and Heat Transfer 35.1(2021):187-199.

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