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Solutions to obstacles in the commercialization of room-temperature magnetic refrigeration

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  • Zhang, Yaokang
  • Wu, Jianghong
  • He, Jing
  • Wang, Kai
  • Yu, Guoxin

Abstract

Most existing room temperature magnetic refrigeration (MR) prototypes are based on the concept of an active magnetic regenerator (AMR). However, for these MR prototypes, three obstacles, i.e. theoretical limit of the MR thermodynamic cycle, low operating frequency, and large irreversible loss during heat regeneration, limit the enhancement of their temperature span and cooling capacity, and further restrict their commercial application. In this paper, the solutions to these obstacles are reviewed from the perspectives of MR thermodynamic cycles and heat transfer enhancement during heat regeneration. With respect to MR cycles, the future trend is likely to be fully solid-state MR cycle and multi-caloric refrigeration cycle. With regard to heat transfer enhancement, the three methods exhibit good practical prospects, namely using liquid metals or nanofluids as the heat transfer fluid, shaping a magnetocaloric material (MCM) into an enhanced heat transfer structure, and inserting materials with high thermal conductivity in the MCM. Moreover, room-temperature MR applications in the cold-storage device, heat pump and electric vehicle air conditioning are reviewed. Small cooling capacity devices are the primary application targets of room-temperature MR, such as the wine cooler, domestic dehumidifier, and portable personal air conditioning.

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  • Zhang, Yaokang & Wu, Jianghong & He, Jing & Wang, Kai & Yu, Guoxin, 2021. "Solutions to obstacles in the commercialization of room-temperature magnetic refrigeration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
  • Handle: RePEc:eee:rensus:v:143:y:2021:i:c:s1364032121002252
    DOI: 10.1016/j.rser.2021.110933
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    3. Shi, Lei & Zhang, Shuai & Arshad, Adeel & Hu, Yanwei & He, Yurong & Yan, Yuying, 2021. "Thermo-physical properties prediction of carbon-based magnetic nanofluids based on an artificial neural network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).

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