详细信息

Performance of high-temperature lightweight multilayer insulations  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Performance of high-temperature lightweight multilayer insulations

作者:Zhou, Qi[1];Wang, Peng[1];Wu, Kede[1];Cao, Junxiang[1];Zhang, Hongyu[1];Zhang, Yayun[1];Niu, Bo[1];Long, Donghui[1]

机构:[1]East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China

年份:2022

卷号:211

外文期刊名:APPLIED THERMAL ENGINEERING

收录:;EI(收录号:20221411925566);WOS:【SCI-EXPANDED(收录号:WOS:000793246300003)】;

基金:Acknowledgements This work is supported by National Natural Science Foundation of China (No. 52102098 and No. 21878091) and the Fundamental Research Funds for the Central Universities.

语种:英文

外文关键词:Multilayer insulation; Thermal insulation performance; Finite element analysis; Heat transfer simulation

摘要:Multilayer insulation (MLI) is a kind of high-temperature insulation which employs multiple high-reflective screens to retard the radiation heat, but analyzing and optimizing its heat transfer characteristics by experimental and theoretical models remains low-efficiency and non-visualization. In this work, finite element analysis (FEA) models verified by comparing the simulation and experiment results are established to systematically reveal the numerous and complex effect factors, including the number of layers, thermal boundary, fibrous insulation density and emissivity of reflective screens. The results show that the top temperature of MLIs can be reduced by increasing the number and the emissivity of reflective screens, and the density of fibrous insulation, but there is a reasonable number of layers to achieve the best comprehensive performance of MLI. In addition, the proportion of radiative heat transfer in the total heat transfer increases with increasing heating temperature. The feasibility of optimizing thermal insulation performance based on the FEA models is confirmed by designing the MLIs with stainless steel and aluminum as reflective screens. And the results indicate that the FEA methods can significantly reduce the number of trials and realize the optimal design of MLI.

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