IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v551y2020ics0378437119322782.html
   My bibliography  Save this article

Experimental evaluation and artificial neural network modeling of thermal conductivity of water based nanofluid containing magnetic copper nanoparticles

Author

Listed:
  • Ghazvini, Mahyar
  • Maddah, Heydar
  • Peymanfar, Reza
  • Ahmadi, Mohammad Hossein
  • Kumar, Ravinder

Abstract

Thermal conductivity of nanofluids performs as a crucial role in heat transfer capacity of fluids. Nanoparticles’ addition to a base fluid results in enhancing thermal conductivity ratio. In this investigation, the thermal conductivity ratio of modified copper nanoparticles/water nanofluid is modeled by applying artificial neural network approaches utilizing experimental data. The nanofluids’ thermal conductivity at various fluid temperatures, nanoparticle concentration and diameter was measured experimentally using KD2Pro. Additionally, in order to model the nanofluid’s thermal conductivity with respect to temperature, solid volume fraction, and diameter, a correlation is proposed by applying artificial neural networks and considering the experimental data. According to statistical accuracy analysis, the best structure to model this task is a two-layer feed-forward ANN model with 6 hidden neurons. This model predicted the experimental data with Mean absolute percentage error (MAPE) of 1.09%, mean square errors (MSE) of 2.5×10−4, and coefficient of determination (R2) of 0.99. Based on the comparison results, the ANN model is able to predict the enhancement in the thermal conductivity of nanofluids favorably.

Suggested Citation

  • Ghazvini, Mahyar & Maddah, Heydar & Peymanfar, Reza & Ahmadi, Mohammad Hossein & Kumar, Ravinder, 2020. "Experimental evaluation and artificial neural network modeling of thermal conductivity of water based nanofluid containing magnetic copper nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437119322782
    DOI: 10.1016/j.physa.2019.124127
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119322782
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.124127?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. Moradikazerouni, Alireza & Hajizadeh, Ahmad & Safaei, Mohammad Reza & Afrand, Masoud & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2019. "Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 138-145.
    2. Mahyari, Amirhossein Ansari & Karimipour, Arash & Afrand, Masoud, 2019. "Effects of dispersed added Graphene Oxide-Silicon Carbide nanoparticles to present a statistical formulation for the mixture thermal properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 98-112.
    3. Safaei, Mohammad Reza & Hajizadeh, Ahmad & Afrand, Masoud & Qi, Cong & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2019. "Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO-TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 209-216.
    4. Wang, X.J. & Li, X.F. & Xu, Y.H. & Zhu, D.S., 2014. "Thermal energy storage characteristics of Cu–H2O nanofluids," Energy, Elsevier, vol. 78(C), pages 212-217.
    5. Toghraie, Davood & Sina, Nima & Jolfaei, Niyusha Adavoodi & Hajian, Mehdi & Afrand, Masoud, 2019. "Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    6. Nafchi, Peyman Mirzakhani & Karimipour, Arash & Afrand, Masoud, 2019. "The evaluation on a new non-Newtonian hybrid mixture composed of TiO2/ZnO/EG to present a statistical approach of power law for its rheological and thermal properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 1-18.
    7. Karimipour, Arash & Bagherzadeh, Seyed Amin & Taghipour, Abdolmajid & Abdollahi, Ali & Safaei, Mohammad Reza, 2019. "A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 89-97.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Wang, Guimei & Mukhtar, Azfarizal & Moayedi, Hossein & Khalilpoor, Nima & Tt, Quynh, 2024. "Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector," Energy, Elsevier, vol. 298(C).

    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. Arani, Ali Akbar Abbasian & Alirezaie, Ali & Kamyab, Mohammad Hassan & Motallebi, Sayyid Majid, 2020. "Statistical analysis of enriched water heat transfer with various sizes of MgO nanoparticles using artificial neural networks modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    2. Roy Setiawan & Reza Daneshfar & Omid Rezvanjou & Siavash Ashoori & Maryam Naseri, 2021. "Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17606-17627, December.
    3. Xiaohong, Dai & Huajiang, Chen & Bagherzadeh, Seyed Amin & Shayan, Masoud & Akbari, Mohammad, 2020. "Statistical estimation the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid using the ridge regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    4. Peng, Yeping & Parsian, Amir & Khodadadi, Hossein & Akbari, Mohammad & Ghani, Kamal & Goodarzi, Marjan & Bach, Quang-Vu, 2020. "Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3 – Cu nanoparticles dispersed in ethy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    5. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
    6. Ali Hassan & Qusain Haider & Najah Alsubaie & Fahad M. Alharbi & Abdullah Alhushaybari & Ahmed M. Galal, 2022. "Investigation of Mixed Convection in Spinning Nanofluid over Rotating Cone Using Artificial Neural Networks and BVP-4C Technique," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
    7. Ahmadi, Mohammad Hossein & Ghazvini, Mahyar & Maddah, Heydar & Kahani, Mostafa & Pourfarhang, Samira & Pourfarhang, Amin & Heris, Saeed Zeinali, 2020. "Prediction of the pressure drop for CuO/(Ethylene glycol-water) nanofluid flows in the car radiator by means of Artificial Neural Networks analysis integrated with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    8. Tian, Zhe & Arasteh, Hossein & Parsian, Amir & Karimipour, Arash & Safaei, Mohammad Reza & Nguyen, Truong Khang, 2019. "Estimate the shear rate & apparent viscosity of multi-phased non-Newtonian hybrid nanofluids via new developed Support Vector Machine method coupled with sensitivity analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    9. Zarei, Amir & Karimipour, Arash & Meghdadi Isfahani, Amir Homayoon & Tian, Zhe, 2019. "Improve the performance of lattice Boltzmann method for a porous nanoscale transient flow by provide a new modified relaxation time equation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    10. Al-Rashed, Abdullah A.A.A., 2019. "Optimization of heat transfer and pressure drop of nano-antifreeze using statistical method of response surface methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 531-542.
    11. Jamei, Mehdi & Ahmadianfar, Iman, 2020. "A rigorous model for prediction of viscosity of oil-based hybrid nanofluids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    12. Tian, Zhe & Rostami, Sara & Taherialekouhi, Roozbeh & Karimipour, Arash & Moradikazerouni, Alireza & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2020. "Prediction of rheological behavior of a new hybrid nanofluid consists of copper oxide and multi wall carbon nanotubes suspended in a mixture of water and ethylene glycol using curve-fitting on experim," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    13. Hemmat Esfe, Mohammad & Esfandeh, Saeed, 2020. "The statistical investigation of multi-grade oil based nanofluids: Enriched by MWCNT and ZnO nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    14. Wu, Huawei & Al-Rashed, Abdullah A.A.A. & Barzinjy, Azeez A. & Shahsavar, Amin & Karimi, Ali & Talebizadehsardari, Pouyan, 2019. "Curve-fitting on experimental thermal conductivity of motor oil under influence of hybrid nano additives containing multi-walled carbon nanotubes and zinc oxide," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    15. Li, Zhixiong & Shahrajabian, Hamzeh & Bagherzadeh, Seyed Amin & Jadidi, Hamid & Karimipour, Arash & Tlili, Iskander, 2020. "Effects of nano-clay content, foaming temperature and foaming time on density and cell size of PVC matrix foam by presented Least Absolute Shrinkage and Selection Operator statistical regression via s," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    16. Wei, Li & Arasteh, Hossein & abdollahi, Ali & Parsian, Amir & Taghipour, Abdolmajid & Mashayekhi, Ramin & Tlili, Iskander, 2020. "Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    17. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    18. 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).
    19. Solangi, K.H. & Kazi, S.N. & Luhur, M.R. & Badarudin, A. & Amiri, A. & Sadri, Rad & Zubir, M.N.M. & Gharehkhani, Samira & Teng, K.H., 2015. "A comprehensive review of thermo-physical properties and convective heat transfer to nanofluids," Energy, Elsevier, vol. 89(C), pages 1065-1086.
    20. Darya S. Loenko & Aroon Shenoy & Mikhail A. Sheremet, 2019. "Natural Convection of Non-Newtonian Power-Law Fluid in a Square Cavity with a Heat-Generating Element," Energies, MDPI, vol. 12(11), pages 1-12, June.

    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:phsmap:v:551:y:2020:i:c:s0378437119322782. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.