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        Fluid Flow and Heat Transfer in Microchannel Heat Sinks: Modelling review and recent progress

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        Accepted_Manuscript.pdf (PDF, 933Kb)(embargoed until 13/01/2023)
        Author
        Gao, Jie
        Hu, Zhuohuan
        Yang, Qiguo
        Liang, Xing
        Wu, Hongwei
        Attention
        2299/25317
        Abstract
        Nowadays, microchannel has been widely utilized in various multidisciplinary fields, and as a consequence, some new and different requirements for microchannels in the process of practical application are required, such as structure, working fluid, and operating conditions, etc. This article reviews the current research achievement of microchannels, as well as the thermodynamic research on microchannels with different structures in the past five years, but mainly focuses on the numerical methods. The purpose of this review article aims to summarize a comprehensive overview of the latest developments of numerical methods in microchannel heat sinks, as well as to provide a useful benchmark for future research. The present article reviews straightforward on the most commonly used numerical methods for solving governing equations and optimizing data, including conventional computational fluid dynamics (CFD) simulation methods, molecular dynamics simulation (MDS), Lattice Boltzmann methods (LBM), direct simulation Monte Carlo (DSMC), and other techniques such as machine learning (ML) approach, artificial neural network (ANN) method, genetic algorithm (GA), Taguchi algorithm (TA), as well as optimisation methods. This review will not only help to understand the physical mechanism of microchannels in different application fields but also help to fill in the gaps in related research and provide research methods for future numerical studies.
        Publication date
        2022-03
        Published in
        Thermal Science and Engineering Progress
        Published version
        https://doi.org/10.1016/j.tsep.2022.101203
        Other links
        http://hdl.handle.net/2299/25317
        Relations
        School of Physics, Engineering & Computer Science
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