A thermal load identification method based on physics-guided neural network for honeycomb sandwich structures

doi:10.1088/1361-665X/acd3c9

CORC > 力学研究所 > 中国科学院力学研究所 > 流固耦合系统力学重点实验室(2012-)

	A thermal load identification method based on physics-guided neural network for honeycomb sandwich structures
	Du WQ(杜文琪); Yang LK(杨乐凯); Lu LL(路玲玲); Le J(乐杰); Yu MK(于明凯); Song HW(宋宏伟); Xing, Xiaodong ; Huang, Chenguang
刊名	SMART MATERIALS AND STRUCTURES ; 7
	2023-07
卷号	32 页码:75008
关键词	thermal load identification physics-guided neural network physics-guided loss function thermal feature parameters laser irradiation
ISSN号	0964-1726
DOI	10.1088/1361-665X/acd3c9
英文摘要	The identification of thermal load/thermal shock of aircraft during service is beneficial for collecting information of the service environment and avoiding risks. In the paper, a method based on multivariate information fusion and physics-guided neural network is developed for the inverse problem of thermal load identification of honeycomb sandwich structures. Two thermal feature parameters: temperature gradient and temperature variation rate are used to build the dataset. A 16-layers physics-guided neural network is presented to achieve the predicted results consistent with physical knowledge. In the work, laser irradiation is used as the thermal load, and two laser parameters are to be identified, i.e. spot diameter, power. Simulations and experiments are conducted to verify the effectiveness of the proposed method. The effects of physics-guided loss function and multivariate information fusion are discussed, and it is found that the results based on the proposed method are much better than the results based on the method without physical model. Besides, results based on multivariate information fusion are better than results based on single temperature response. Then, the effects of network models and hyper parameters on the proposed method are also discussed.
分类号	二类
WOS研究方向	WOS:000999625700001
语种	英语
资助机构	National Natural Science Foundation of China [11972033, 12272379] ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA22000000]
其他责任者	Lu, LL
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/92250]
专题	力学研究所_流固耦合系统力学重点实验室(2012-)
作者单位	1.(Yang Lekai, Le Jie, Yu Mingkai, Huang Chenguang) Harbin Engn Univ Sch Mech & Elect Engn Harbin 150001 Peoples R China 2.(Du Wenqi, Lu Lingling, Song Hongwei, Xing Xiaodong) Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China 3.(Du Wenqi, Yang Lekai, Lu Lingling, Le Jie, Yu Mingkai, Song Hongwei) Chinese Acad Sci Inst Mech Key Lab Mech Fluid Solid Coupling Syst Beijing 100190 Peoples R China
推荐引用方式 GB/T 7714	Du WQ,Yang LK,Lu LL,et al. A thermal load identification method based on physics-guided neural network for honeycomb sandwich structures[J]. SMART MATERIALS AND STRUCTURES, 7,2023,32:75008.
APA	杜文琪.,杨乐凯.,路玲玲.,乐杰.,于明凯.,...&Huang, Chenguang.(2023).A thermal load identification method based on physics-guided neural network for honeycomb sandwich structures.SMART MATERIALS AND STRUCTURES,32,75008.
MLA	杜文琪,et al."A thermal load identification method based on physics-guided neural network for honeycomb sandwich structures".SMART MATERIALS AND STRUCTURES 32(2023):75008.