IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v149y2021ics1364032121006274.html
   My bibliography  Save this article

Thermo-physical properties prediction of carbon-based magnetic nanofluids based on an artificial neural network

Author

Listed:
  • Shi, Lei
  • Zhang, Shuai
  • Arshad, Adeel
  • Hu, Yanwei
  • He, Yurong
  • Yan, Yuying

Abstract

Nanostructured magnetic suspensions have superior thermophysical properties, which have attracted widespread attention owing to their industrial applications for heat transfer enhancement and thermal management. However, experimental measurements of the thermophysical properties of magnetic-based nanofluids, especially under an external magnetic field, are significantly complicated, expensive, and time consuming. Currently, the method of predicting and summarizing material properties through machine learning has accelerated the development of materials and practical industrial applications. This study aims to predict the thermophysical properties of magnetic nanofluids by establishing an artificial neural network (ANN) using experimental data on viscosity, thermal conductivity, and specific heat. The results based on the ANN model agree with the experimental results according to the different evaluation criteria. Different previous theoretical thermophysical models are reviewed, and the ANN model is proven to be more accurate by comparing the values of the ANN model and previous thermophysical models, which can also provide a theoretical basis for explaining the heat transfer of magnetic nanofluids. In the present study, a neural network model was developed for predicting the thermophysical properties of magnetic nanofluids and using material informatics to study functional materials.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:149:y:2021:i:c:s1364032121006274
    DOI: 10.1016/j.rser.2021.111341
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032121006274
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2021.111341?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Said, Zafar & El Haj Assad, M. & Hachicha, Ahmed Amine & Bellos, Evangelos & Abdelkareem, Mohammad Ali & Alazaizeh, Duha Zeyad & Yousef, Bashria A.A., 2019. "Enhancing the performance of automotive radiators using nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 183-194.
    2. 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).
    3. Kaikai Guo & Fucheng Chang & Huixiong Li, 2021. "Application of a Magnetic Field in Saturated Film Boiling of a Magnetic Nanofluid (MNF) under Reduced Gravity," Energies, MDPI, vol. 14(3), pages 1-24, January.
    4. Lv, Peizhao & Liu, Chenzhen & Rao, Zhonghao, 2017. "Review on clay mineral-based form-stable phase change materials: Preparation, characterization and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 707-726.
    5. Khan, M. Ijaz & Qayyum, Sumaira & Farooq, Shahid & Chu, Yu-Ming & Kadry, Seifedine, 2021. "Modeling and simulation of micro-rotation and spin gradient viscosity for ferromagnetic hybrid (Manganese Zinc Ferrite, Nickle Zinc Ferrite) nanofluids," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 497-509.
    6. Aladag, Bahadir & Halelfadl, Salma & Doner, Nimeti & Maré, Thierry & Duret, Steven & Estellé, Patrice, 2012. "Experimental investigations of the viscosity of nanofluids at low temperatures," Applied Energy, Elsevier, vol. 97(C), pages 876-880.
    7. Ganvir, R.B. & Walke, P.V. & Kriplani, V.M., 2017. "Heat transfer characteristics in nanofluid—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 451-460.
    8. Vanaki, Sh.M. & Ganesan, P. & Mohammed, H.A., 2016. "Numerical study of convective heat transfer of nanofluids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1212-1239.
    9. Shi, Lei & Hu, Yanwei & Bai, Yijie & He, Yurong, 2020. "Dynamic tuning of magnetic phase change composites for solar-thermal conversion and energy storage," Applied Energy, Elsevier, vol. 263(C).
    10. Hussein, Adnan M. & Sharma, K.V. & Bakar, R.A. & Kadirgama, K., 2014. "A review of forced convection heat transfer enhancement and hydrodynamic characteristics of a nanofluid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 734-743.
    11. 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.
    12. Sajid, Muhammad Usman & Ali, Hafiz Muhammad, 2019. "Recent advances in application of nanofluids in heat transfer devices: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 556-592.
    13. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    14. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    15. Rahimikhoob, Ali, 2010. "Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment," Renewable Energy, Elsevier, vol. 35(9), pages 2131-2135.
    16. Al-Waeli, Ali H.A. & Kazem, Hussein A. & Yousif, Jabar H. & Chaichan, Miqdam T. & Sopian, K., 2020. "Mathematical and neural network modeling for predicting and analyzing of nanofluid-nano PCM photovoltaic thermal systems performance," Renewable Energy, Elsevier, vol. 145(C), pages 963-980.
    17. Azmi, W.H. & Sharma, K.V. & Mamat, Rizalman & Najafi, G. & Mohamad, M.S., 2016. "The enhancement of effective thermal conductivity and effective dynamic viscosity of nanofluids – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1046-1058.
    18. Lin, Cherng-Yuan & Wang, Jung-Chang & Chen, Teng-Chieh, 2011. "Analysis of suspension and heat transfer characteristics of Al2O3 nanofluids prepared through ultrasonic vibration," Applied Energy, Elsevier, vol. 88(12), pages 4527-4533.
    19. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    20. Kurt, Hüseyin & Kayfeci, Muhammet, 2009. "Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks," Applied Energy, Elsevier, vol. 86(10), pages 2244-2248, October.
    21. Wang, Liang & Lin, Xipeng & Chai, Lei & Peng, Long & Yu, Dong & Chen, Haisheng, 2019. "Cyclic transient behavior of the Joule–Brayton based pumped heat electricity storage: Modeling and analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 523-534.
    22. Nkurikiyimfura, Innocent & Wang, Yanmin & Pan, Zhidong, 2013. "Heat transfer enhancement by magnetic nanofluids—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 548-561.
    23. Gupta, Munish & Singh, Vinay & Kumar, Rajesh & Said, Z., 2017. "A review on thermophysical properties of nanofluids and heat transfer applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 638-670.
    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. Ambreen, Tehmina & Kim, Man-Hoe, 2018. "Heat transfer and pressure drop correlations of nanofluids: A state of art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 564-583.
    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. Xu, Yanyan & Xue, Yanqin & Qi, Hong & Cai, Weihua, 2021. "An updated review on working fluids, operation mechanisms, and applications of pulsating heat pipes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Gürdal, Mehmet & Arslan, Kamil & Gedik, Engin & Minea, Alina Adriana, 2022. "Effects of using nanofluid, applying a magnetic field, and placing turbulators in channels on the convective heat transfer: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Elsheikh, A.H. & Sharshir, S.W. & Mostafa, Mohamed E. & Essa, F.A. & Ahmed Ali, Mohamed Kamal, 2018. "Applications of nanofluids in solar energy: A review of recent advances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3483-3502.
    6. Ma, Ting & Guo, Zhixiong & Lin, Mei & Wang, Qiuwang, 2021. "Recent trends on nanofluid heat transfer machine learning research applied to renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    7. He, Ziqiang & Yan, Yunfei & Zhang, Zhien, 2021. "Thermal management and temperature uniformity enhancement of electronic devices by micro heat sinks: A review," Energy, Elsevier, vol. 216(C).
    8. 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.
    9. Bigdeli, Masoud Bozorg & Fasano, Matteo & Cardellini, Annalisa & Chiavazzo, Eliodoro & Asinari, Pietro, 2016. "A review on the heat and mass transfer phenomena in nanofluid coolants with special focus on automotive applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1615-1633.
    10. Coccia, Gianluca & Tomassetti, Sebastiano & Di Nicola, Giovanni, 2021. "Thermal conductivity of nanofluids: A review of the existing correlations and a scaled semi-empirical equation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    11. Shah, Tayyab Raza & Ali, Hafiz Muhammad & Zhou, Chao & Babar, Hamza & Janjua, Muhammad Mansoor & Doranehgard, Mohammad Hossein & Hussain, Abid & Sajjad, Uzair & Wang, Chi-Chuan & Sultan, Muhamad, 2022. "Potential evaluation of water-based ferric oxide (Fe2O3-water) nanocoolant: An experimental study," Energy, Elsevier, vol. 246(C).
    12. Mukkamala, Yagnavalkya, 2017. "Contemporary trends in thermo-hydraulic testing and modeling of automotive radiators deploying nano-coolants and aerodynamically efficient air-side fins," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1208-1229.
    13. 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.
    14. Colangelo, Gianpiero & Favale, Ernani & de Risi, Arturo & Laforgia, Domenico, 2013. "A new solution for reduced sedimentation flat panel solar thermal collector using nanofluids," Applied Energy, Elsevier, vol. 111(C), pages 80-93.
    15. Rostami, Sara & Afrand, Masoud & Shahsavar, Amin & Sheikholeslami, M. & Kalbasi, Rasool & Aghakhani, Saeed & Shadloo, Mostafa Safdari & Oztop, Hakan F., 2020. "A review of melting and freezing processes of PCM/nano-PCM and their application in energy storage," Energy, Elsevier, vol. 211(C).
    16. Said, Zafar & El Haj Assad, M. & Hachicha, Ahmed Amine & Bellos, Evangelos & Abdelkareem, Mohammad Ali & Alazaizeh, Duha Zeyad & Yousef, Bashria A.A., 2019. "Enhancing the performance of automotive radiators using nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 183-194.
    17. Tomasz Halon & Ewa Pelinska-Olko & Malgorzata Szyc & Bartosz Zajaczkowski, 2019. "Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network," Energies, MDPI, vol. 12(17), pages 1-11, August.
    18. Sundar, L. Syam & Sharma, K.V. & Singh, Manoj K. & Sousa, A.C.M., 2017. "Hybrid nanofluids preparation, thermal properties, heat transfer and friction factor – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 185-198.
    19. Sadegh Hosseini, Seyed Mohammad & Dehaj, Mohammad Shafiey, 2021. "An experimental study on energetic performance evaluation of a parabolic trough solar collector operating with Al2O3/water and GO/water nanofluids," Energy, Elsevier, vol. 234(C).
    20. Selimefendigil, Fatih & Öztop, Hakan F., 2021. "Thermoelectric generation in bifurcating channels and efficient modeling by using hybrid CFD and artificial neural networks," Renewable Energy, Elsevier, vol. 172(C), pages 582-598.

    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:eee:rensus:v:149:y:2021:i:c:s1364032121006274. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.