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A Gaussian process regression accelerated multiscale model for conduction-radiation heat transfer in periodic composite materials with temperature-dependent thermal properties

作     者:Zi-Xiang Tong Ming-Jia Li Zhaolin Gu Jun-Jie Yan Wen-Quan Tao 

作者机构:School of Human Settlements and Civil EngineeringXi’an Jiaotong UniversityXi’an710049ShaanxiChina Key Laboratory of Thermo-Fluid Science and Engineering of Ministry of EducationSchool of Energy and Power EngineeringXi’an Jiaotong UniversityXi’an710049ShaanxiChina School of Energy and Power EngineeringXi’an Jiaotong UniversityXi’an710049ShaanxiChina 

出 版 物:《Advances in Aerodynamics》 (空气动力学进展(英文))

年 卷 期:2022年第4卷第1期

页      面:642-661页

核心收录:

学科分类:0401[教育学-教育学] 04[教育学] 

基  金:National Numerical Windtunnel Project of China(NNW2018ZT2-A04,NNW2020ZT3-A22) National Natural Science Foundation of China(No.51906186) 

主  题:Multiscale model Heat Conduction Radiative transfer equation Temperature-dependent Gaussian process regression Machine learning 

摘      要:Prediction of the coupled conduction-radiation heat transfer in composite materials with periodic structure is important in high-temperature applications of the materials. The temperature dependence of thermal properties complicates the problem. In this work, a multiscale model is proposed for the conduction-radiation heat transfer in periodic composite materials with temperature-dependent thermal properties. Homogenization analysis of the coupled conduction and radiative transfer equations is conducted, in which the temperature dependence of thermal properties is considered. Both the macroscopic homogenized equations and the local unit cell problems are derived. It is proved that the macroscopic average temperature can be used in the unit cell problems for the first-order corrections of the temperature and radiative intensity, and the calculations of effective thermal properties. The temperature dependence of thermal properties only influences the higher-order corrections. A multiscale numerical method is proposed based on the analysis. The Gaussian process (GP) regression is coupled into the multiscale algorithm to build a correlation between thermal properties and temperature for the macroscale iterations and prevent the repetitive solving of unit cell problems. The GP model is updated by additional solutions of unit cell problems during the iteration according to a variance threshold. Numerical simulations of conduction-radiation heat transfer in composite with isotropic and anisotropic periodic structures are used to validate the proposed multiscale model. It is found that the accuracy and efficiency of the multiscale method can be guaranteed by using a proper variance threshold for the GP model. The multiscale model can provide both the average temperature and radiative intensity fields and their detailed fluctuations due to the local structures.

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