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A flexible numerical model to study an active magnetic refrigerator for near room temperature applications

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  • Aprea, Ciro
  • Maiorino, Angelo

Abstract

Magnetic refrigeration is an emerging technology based on the magnetocaloric effect in solid-state refrigerants. This technology offers a smaller global environmental impact than the refrigeration obtained by means of the classical vapor compression machines operating with fluids such as HFCs. The Active Magnetic Regenerative Refrigeration (AMRR) is currently the most studied ant tested magnetic cycle. It combines the regenerative properties of a high specific heat solid porous matrix with the ability of performing thermo-magnetic cycles thanks to the magnetocaloric property of the refrigerant; while a fluid pulsing through the regenerator works as a heat transfer medium. An active magnetic regenerator can provide larger temperature spans making up for the local small temperature variation of the refrigerant. In the present paper, a practical model for predicting the performance and efficiency of an AMRR cycle has been developed. The model evaluates both the refrigerant properties and the entire cycle of an AMR operating in conformity with a Brayton regenerative cycle. The magnetocaloric material of choice is gadolinium, while the heat transfer medium is liquid water. With this model can be predicted the refrigeration capacity, the power consumption and consequently the Coefficient of Performance. The results show a greater COP when compared to a classical vapor compression plant working between the same temperature levels.

Suggested Citation

  • Aprea, Ciro & Maiorino, Angelo, 2010. "A flexible numerical model to study an active magnetic refrigerator for near room temperature applications," Applied Energy, Elsevier, vol. 87(8), pages 2690-2698, August.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:8:p:2690-2698
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    References listed on IDEAS

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    1. Xia, Zhengrong & Zhang, Yue & Chen, Jincan & Lin, Guoxing, 2008. "Performance analysis and parametric optimal criteria of an irreversible magnetic Brayton-refrigerator," Applied Energy, Elsevier, vol. 85(2-3), pages 159-170, February.
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    Cited by:

    1. Kefayati, G.H.R., 2016. "Simulation of double diffusive MHD (magnetohydrodynamic) natural convection and entropy generation in an open cavity filled with power-law fluids in the presence of Soret and Dufour effects (Part I: S," Energy, Elsevier, vol. 107(C), pages 889-916.
    2. Silva, D.J. & Bordalo, B.D. & Pereira, A.M. & Ventura, J. & Araújo, J.P., 2012. "Solid state magnetic refrigerator," Applied Energy, Elsevier, vol. 93(C), pages 570-574.
    3. Kefayati, G.H.R., 2016. "Simulation of double diffusive MHD (magnetohydrodynamic) natural convection and entropy generation in an open cavity filled with power-law fluids in the presence of Soret and Dufour effects (part II: ," Energy, Elsevier, vol. 107(C), pages 917-959.
    4. Lozano, J.A. & Engelbrecht, K. & Bahl, C.R.H. & Nielsen, K.K. & Eriksen, D. & Olsen, U.L. & Barbosa, J.R. & Smith, A. & Prata, A.T. & Pryds, N., 2013. "Performance analysis of a rotary active magnetic refrigerator," Applied Energy, Elsevier, vol. 111(C), pages 669-680.
    5. Silva, D.J. & Ventura, J. & Araújo, J.P. & Pereira, A.M., 2014. "Maximizing the temperature span of a solid state active magnetic regenerative refrigerator," Applied Energy, Elsevier, vol. 113(C), pages 1149-1154.
    6. Maiorino, Angelo & Del Duca, Manuel Gesù & Aprea, Ciro, 2022. "ART.I.CO. (ARTificial Intelligence for COoling): An innovative method for optimizing the control of refrigeration systems based on Artificial Neural Networks," Applied Energy, Elsevier, vol. 306(PB).
    7. Angelo Maiorino & Manuel Gesù Del Duca & Jaka Tušek & Urban Tomc & Andrej Kitanovski & Ciro Aprea, 2019. "Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks," Energies, MDPI, vol. 12(10), pages 1-22, May.
    8. Aprea, C. & Greco, A. & Maiorino, A. & Masselli, C., 2018. "Solid-state refrigeration: A comparison of the energy performances of caloric materials operating in an active caloric regenerator," Energy, Elsevier, vol. 165(PA), pages 439-455.
    9. Qian, Suxin & Yuan, Lifen & Yu, Jianlin & Yan, Gang, 2018. "Variable load control strategy for room-temperature magnetocaloric cooling applications," Energy, Elsevier, vol. 153(C), pages 763-775.
    10. Trevizoli, Paulo V. & Nakashima, Alan T. & Peixer, Guilherme F. & Barbosa, Jader R., 2017. "Performance assessment of different porous matrix geometries for active magnetic regenerators," Applied Energy, Elsevier, vol. 187(C), pages 847-861.
    11. Ismail, A. & Perrin, M. & Giurgea, S. & Bailly, Y. & Roy, J.C. & Barriere, T., 2022. "Multiphysical and multidimensional modelling of Parallel-Plate active magnetic regenerator," Applied Energy, Elsevier, vol. 314(C).
    12. Zhao, Guimei & Geng, Yong & Wei, Wendong & Bleischwitz, Raimund & Ge, Zewen, 2023. "Assessing gadolinium resource efficiency and criticality in China," Resources Policy, Elsevier, vol. 80(C).
    13. Kamran, Muhammad Sajid & Ahmad, Hafiz Ozair & Wang, Hua Sheng, 2020. "Review on the developments of active magnetic regenerator refrigerators – Evaluated by performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    14. Kotani, Yui & Kansha, Yasuki & Tsutsumi, Atsushi, 2013. "Conceptual design of an active magnetic regenerative heat circulator based on self-heat recuperation technology," Energy, Elsevier, vol. 55(C), pages 127-133.
    15. Teyber, Reed & Holladay, Jamelyn & Meinhardt, Kerry & Polikarpov, Evgueni & Thomsen, Edwin & Cui, Jun & Rowe, Andrew & Barclay, John, 2019. "Performance investigation of a high-field active magnetic regenerator," Applied Energy, Elsevier, vol. 236(C), pages 426-436.

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