p A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials

doi:10.4208/cicp.OA-2021-0110

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	p A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials
	Dong, Hao 1; Kou, Wenbo 1; Han, Junyan 2; Linghu, Jiale 1; Zou, Minqiang 3; Cui, Junzhi 4
	2022-02-01
发表期刊	COMMUNICATIONS IN COMPUTATIONAL PHYSICS
ISSN	1815-2406
卷号	31 期号:2 页码:593-625
摘要	In this paper, a novel mixed wavelet-learning method is developed for predicting macroscopic effective heat transfer conductivities of braided composite materials with heterogeneous thermal conductivity. This innovative methodology integrates respective superiorities of multi-scale modeling, wavelet transform and neural networks together. By the aid of asymptotic homogenization method (AHM), off-line multi-scale modeling is accomplished for establishing the material database with highdimensional and highly-complex mappings. The multi-scale material database and the wavelet-learning strategy ease the on-line training of neural networks, and enable us to efficiently build relatively simple networks that have an essentially increasing capacity and resisting noise for approximating the high-complexity mappings. Moreover, it should be emphasized that the wavelet-learning strategy can not only extract essential data characteristics from the material database, but also achieve a tremendous reduction in input data of neural networks. The numerical experiments performed using multiple 3D braided composite models verify the excellent performance of the presented mixed approach. The numerical results demonstrate that the mixed waveletlearning methodology is a robust method for computing the macroscopic effective heat transfer conductivities with distinct heterogeneity patterns. The presented method can enormously decrease the computational time, and can be further expanded into estimating macroscopic effective mechanical properties of braided composites.
关键词	Braided composite materials macroscopic effective heat transfer conductivities multi-scale modeling neural networks wavelet transform
DOI	10.4208/cicp.OA-2021-0110
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[51739007] ; National Natural Science Foundation of China[61971328] ; National Natural Science Foundation of China[12001414] ; Fundamental Research Funds for the Central Universities[JB210702] ; open foundation of Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics (Wuhan University of Technology)[WUT-TAM202104] ; China Postdoctoral Science Foundation[2018M643573] ; Natural Science Basic Research Program of Shaanxi Province[2019JQ-048] ; Center for high performance computing of Xidian University
WOS研究方向	Physics
WOS类目	Physics, Mathematical
WOS记录号	WOS:000746990000001
出版者	GLOBAL SCIENCE PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59927
专题	中国科学院数学与系统科学研究院
通讯作者	Dong, Hao; Cui, Junzhi
作者单位	1.Xidian Univ, Sch Math & Stat, Xian 710071, Peoples R China 2.Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China 3.Xidian Univ, Sch Mechano Elect Engn, Xian 710071, Peoples R China 4.Chinese Acad Sci, LSEC, ICMSEC, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Dong, Hao,Kou, Wenbo,Han, Junyan,et al. p A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials[J]. COMMUNICATIONS IN COMPUTATIONAL PHYSICS,2022,31(2):593-625.
APA	Dong, Hao,Kou, Wenbo,Han, Junyan,Linghu, Jiale,Zou, Minqiang,&Cui, Junzhi.(2022).p A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials.COMMUNICATIONS IN COMPUTATIONAL PHYSICS,31(2),593-625.
MLA	Dong, Hao,et al."p A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials".COMMUNICATIONS IN COMPUTATIONAL PHYSICS 31.2(2022):593-625.