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Author：

Shan, Q. (Shan, Q..) | Ding, Y. (Ding, Y..) | Wang, H. (Wang, H..) | Zhou, X. (Zhou, X..) | Xu, Y. (Xu, Y..) | Wang, J. (Wang, J..) | Xia, G. (Xia, G..)

Indexed by：

Scopus

Abstract：

A　bilayer　thermal　cloak　consists　of　an　inner　layer　with　very　low　thermal　conductivity　and　an　outer　layer　with　higher　thermal　conductivity,　which　can　eliminate　the　external-field　distortion.　However,　the　thermal　cloak　phenomenon　could　not　be　achieved　for　lower　surrounding　thermal　conductivity　in　the　bilayer　thermal　cloak,　because　the　inner　layer　could　not　be　completely　insulated.　In　this　paper,　based　on　the　method　of　machine　learning,　the　performance　of　the　bilayer　thermal　cloak　has　been　optimized.　An　artificial　neural　network　is　established　to　intelligently　learn　the　relation　between　each　layer’s　thermal　conductivity　and　the　cloaking　performances.　The　optimized　parameters　for　a　better　performance　of　the　bilayer　thermal　cloak　can　be　obtained,　which　is　verified　by　numerical　simulations.　©　2025　Science　Press.　All　rights　reserved.

Keyword：

simulation thermal cloak artificial neural network thermal conductivity

Author Community：

  • [ 1 ] [Shan Q.]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Ding Y.]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Wang H.]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Zhou X.]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Beijing University of Technology, Beijing, 100124, China
  • [ 5 ] [Xu Y.]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Beijing University of Technology, Beijing, 100124, China
  • [ 6 ] [Wang J.]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Beijing University of Technology, Beijing, 100124, China
  • [ 7 ] [Xia G.]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Beijing University of Technology, Beijing, 100124, China

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Source ：

Journal of Engineering Thermophysics

ISSN： 0253-231X

Year： 2025

Issue： 3

Volume： 46

Page： 982-987

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 9

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