IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i10p1871-d231830.html
   My bibliography  Save this article

Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks

Author

Listed:
  • Angelo Maiorino

    (Department of Industrial Engineering, Università di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Salerno, Italy)

  • Manuel Gesù Del Duca

    (Department of Industrial Engineering, Università di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Salerno, Italy)

  • Jaka Tušek

    (Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, 1000 Ljubljana, Slovenia)

  • Urban Tomc

    (Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, 1000 Ljubljana, Slovenia)

  • Andrej Kitanovski

    (Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, 1000 Ljubljana, Slovenia)

  • Ciro Aprea

    (Department of Industrial Engineering, Università di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Salerno, Italy)

Abstract

The thermodynamic characterisation of magnetocaloric materials is an essential task when evaluating the performance of a cooling process based on the magnetocaloric effect and its application in a magnetic refrigeration cycle. Several methods for the characterisation of magnetocaloric materials and their thermodynamic properties are available in the literature. These can be generally divided into theoretical and experimental methods. The experimental methods can be further divided into direct and indirect methods. In this paper, a new procedure based on an artificial neural network to predict the thermodynamic properties of magnetocaloric materials is reported. The results show that the procedure provides highly accurate predictions of both the isothermal entropy and the adiabatic temperature change for two different groups of magnetocaloric materials that were used to validate the procedure. In comparison with the commonly used techniques, such as the mean field theory or the interpolation of experimental data, this procedure provides highly accurate, time-effective predictions with the input of a small amount of experimental data. Furthermore, this procedure opens up the possibility to speed up the characterisation of new magnetocaloric materials by reducing the time required for experiments.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1871-:d:231830
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/10/1871/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/10/1871/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark O. McLinden & J. Steven Brown & Riccardo Brignoli & Andrei F. Kazakov & Piotr A. Domanski, 2017. "Limited options for low-global-warming-potential refrigerants," Nature Communications, Nature, vol. 8(1), pages 1-9, April.
    2. 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.
    3. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    3. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    4. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    5. Liu, Hua & Zhao, Baiyang & Zhang, Zhiping & Li, Hongbo & Hu, Bin & Wang, R.Z., 2020. "Experimental validation of an advanced heat pump system with high-efficiency centrifugal compressor," Energy, Elsevier, vol. 213(C).
    6. He, Yijian & Jiang, Yunyun & Fan, Yuchen & Chen, Guangming & Tang, Liming, 2020. "Utilization of ultra-low temperature heat by a novel cascade refrigeration system with environmentally-friendly refrigerants," Renewable Energy, Elsevier, vol. 157(C), pages 204-213.
    7. Mohanraj, M. & Belyayev, Ye. & Jayaraj, S. & Kaltayev, A., 2018. "Research and developments on solar assisted compression heat pump systems – A comprehensive review (Part A: Modeling and modifications)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 90-123.
    8. 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).
    9. 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.
    10. Tomasz Tietze & Piotr Szulc & Daniel Smykowski & Andrzej Sitka & Romuald Redzicki, 2021. "Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations," Energies, MDPI, vol. 14(12), pages 1-17, June.
    11. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
    12. Sovacool, Benjamin K. & Griffiths, Steve & Kim, Jinsoo & Bazilian, Morgan, 2021. "Climate change and industrial F-gases: A critical and systematic review of developments, sociotechnical systems and policy options for reducing synthetic greenhouse gas emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    13. Yang, Fufang & Yang, Fubin & Liu, Qiang & Chu, Qingfu & Yang, Zhen & Duan, Yuanyuan, 2022. "Thermodynamic analysis of working fluids: What is the highest performance of the sub- and trans-critical organic Rankine cycles?," Energy, Elsevier, vol. 241(C).
    14. Albà, C.G. & Alkhatib, I.I.I. & Llovell, F. & Vega, L.F., 2023. "Hunting sustainable refrigerants fulfilling technical, environmental, safety and economic requirements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    15. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    16. Wang, Bo & Chao, Yijun & Zhao, Qinyu & Wang, Haoren & Wang, Yabin & Gan, Zhihua, 2021. "A high efficiency stirling-type pulse tube refrigerator for cooling above 200 K," Energy, Elsevier, vol. 215(PB).
    17. 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.
    18. Wang, Ziyu & Lu, Zhenyu & Yelishala, Sai C. & Metghalchi, Hameed & Levendis, Yiannis A., 2021. "Flame characteristics of propane-air-carbon dioxide blends at elevated temperatures and pressures," Energy, Elsevier, vol. 228(C).
    19. Selim Karkour & Tomohiko Ihara & Tadahiro Kuwayama & Kazuki Yamaguchi & Norihiro Itsubo, 2021. "Life Cycle Assessment of Residential Air Conditioners Considering the Benefits of Their Use: A Case Study in Indonesia," Energies, MDPI, vol. 14(2), pages 1-18, January.
    20. Song, Mengjie & Deng, Shiming & Dang, Chaobin & Mao, Ning & Wang, Zhihua, 2018. "Review on improvement for air source heat pump units during frosting and defrosting," Applied Energy, Elsevier, vol. 211(C), pages 1150-1170.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1871-:d:231830. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.