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Thermal Behavior of Magnetite Nanofluid under Magnetic Field: An Experimental Study and Development of Predictive Model to Predict Thermal Conductivity

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Journal of Engineering Thermophysics Aims and scope

Abstract

Heat transfer characteristic of nanofluid has fascinated numerous researchers. Several parameters affecting it are being investigated. In this study, the thermal conductivity of low concentrated magnetite nanofluid under magnetic field is determined experimentally. The nanofluid is prepared using two-step method and its thermal conductivity is measured at different temperature (10°C–70°C), particle concentration (0.01%–0.1%) and magnetic field (0–700 G). Maximum rise of 24% in thermal conductivity is observed compared to water. Despite the fact that the magnetic field increases the thermal conductivity of the nanofluid, it is theoretically determined that the fluid’s mobility is impeded. As a result, coming to a strong judgement is challenging. As experimental investigation to determine the thermal conductivity is time-consuming and cost-ineffective. This study also proposes a novel method based on Gaussian process regression to predict the thermal conductivity. While developing this model, various kernel functions are tested, and the Matern kernel function is found to be the best based on root mean square value and regression coefficient value. Another model is developed using Artificial neural network (ANN) model. While developing the ANN model different number of neurons in hidden layer has been tested along with different training function. Prediction is found to be best with six neurons in the hidden layer and Levenberg–Marquardt as the training function. The accuracy of the developed model may be used for the prediction of the thermal conductivity under different conditions which may reduce the experimental run cost.

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REFERENCES

  1. Bahiraei, M. and Heshmatian, S., Electronics Cooling with Nanofluids: A Critical Review, Energy Convers. Manag., 2018, vol. 172, pp. 438–456; https://doi.org/10.1016/j.enconman.2018.07.047.

    Article  Google Scholar 

  2. Sidik, N.A.C., Yazid, M.N.A.W.M., and Mamat, R., A Review on the Application of Nanofluids in Vehicle Engine Cooling System, Int. Comm. Heat Mass Transfer, 2015, vol. 68, pp. 85–90; https://doi.org/10.1016/ j.icheatmasstransfer.2015.08.017.

    Article  Google Scholar 

  3. Raj, P. and Subudhi, S., A Review of Studies Using Nanofluids in Flat-Plate and Direct Absorption Solar Collectors, Renew. Sustain. Energy Rev., 2018, vol. 84, pp. 54–74; https://doi.org/10.1016/ j.rser.2017.10.012.

    Article  Google Scholar 

  4. Kumar, V., Tiwari, A.K., and Ghosh, S.K., Application of Nanofluids in Plate Heat Exchanger: A Review, Energy Convers. Manag., 2015, vol. 105, pp. 1017–1036; https://doi.org/10.1016/j.enconman.2015.08.053.

    Article  Google Scholar 

  5. Sharifi, I., Shokrollahi, H., and Amiri, S., Ferrite-Based Magnetic Nanofluids Used in Hyperthermia Applications, J. Magn. Magn. Mater., 2012, vol. 324, no. 6, pp. 903–915; https://doi.org/10.1016/ j.jmmm.2011.10.017.

    Article  ADS  Google Scholar 

  6. Zhou, C., Guo, X., Zhang, K., Cheng, L., and Wu, Y., The Coupling Effect of Micro-Groove Textures and Nanofluids on Cutting Performance of Uncoated Cemented Carbide Tools in Milling Ti-6Al-4V, J. Mater. Process. Technol., 2019, vol. 271(November 2018), pp. 36–45; https://doi.org/10.1016/ j.jmatprotec.2019.03.021.

    Article  Google Scholar 

  7. Guo, X., Huang, Q., Wang, C., Liu, T., Zhang, Y., He, H., and Zhang, K., Effect of Magnetic Field on Cutting Performance of Micro-Textured Tools under Fe3O4 Nanofluid Lubrication Condition, J. Mater. Process. Technol., 2022, vol. 299, no. 8, p. 117382; https://doi.org/10.1016/j.jmatprotec.2021.117382.

    Article  Google Scholar 

  8. Omiddezyani, S., Yousefi-Asli, V., Houshfar, E., Gharehkhani, S., Ashjaee, M., and Khazaee, I., On-Demand Heat Transfer Augmentation Using Magnetically Triggered Ferrofluid Containing Eco-Friendly Treated CoFe2O4/RGO, Powder Technol., 2021, vol. 378, pp. 468–486; https://doi.org/10.1016/ j.powtec.2020.10.030.

    Article  Google Scholar 

  9. Zhong, J.-F., Sedeh, S.N., Lv, Y.-P., Arzani, B., and Toghraie, D., Investigation of Ferro-Nanofluid Flow within a Porous Ribbed Microchannel Heat Sink Using Single-Phase and Two-Phase Approaches in the Presence of Constant Magnetic Field, Powder Technol., 2021, vol. 387, pp. 251–260; https://doi.org/ 10.1016/j.powtec.2021.04.033.

    Article  Google Scholar 

  10. Kumar, D., Kumar, A., and Subudhi, S., Effect of Spatially Varying Magnetic Field on the Cooling of an Electronic Component by Natural Convection With Magnetic Nanofluids, J. Therm. Sci. Eng. Appl., 2021, vol. 13, no. 6; https://doi.org/10.1115/1.4050233.

  11. Kumar, D. and Subudhi, S., Numerical Investigation of Twin-Fins of Different Materials on Buoyancy Induced Convection in Magnetite Nanofluid under Magnetic Field, Comput. Thermal Sci.: An Int. J., 2022, vol. 15, no. 1, pp. 51–73; https://doi.org/10.1615/ComputThermalScien.2022041973.

    Article  Google Scholar 

  12. Parekh, K. and Lee, H.S., Magnetic Field Induced Enhancement in Thermal Conductivity of Magnetite Nanofluid, J. Appl. Phys., 2010, vol. 107, vol. 9; https://doi.org/10.1063/1.3348387.

    Article  Google Scholar 

  13. Philip, J., Shima, P.D., and Raj, B., Enhancement of Thermal Conductivity in Magnetite Based Nanofluid Due to Chainlike Structures Enhancement of Thermal Conductivity in Magnetite Based Nanofluid Due to Chainlike Structures, Appl. Phys. Lett., 2007, vol. 91, no. 20; https://doi.org/10.1063/1.2812699.

    Article  ADS  Google Scholar 

  14. Philip, J., Shima, P.D., and Raj, B., Nanofluid with Tunable Thermal Properties, Appl. Phys. Lett., 2008, vol. 92, no. 4, pp. 10–13.

    Article  Google Scholar 

  15. Shima, P.D., Philip, J., Raj, B., Magnetically Controllable Nanofluid with Tunable Thermal Conductivity and Viscosity, Appl. Phys. Lett., vol. 95, no. 13; https://doi.org/10.1063/1.3238551.

    Article  ADS  Google Scholar 

  16. Sundar, L.S., Singh, M.K., and Sousa, A.C.M., Investigation of Thermal Conductivity and Viscosity of Fe3O4 Nanofluid for Heat Transfer Applications, Int. Comm. Heat Mass Transfer, 2013, vol. 44, pp. 7–14; https://doi.org/10.1016/j.icheatmasstransfer.2013.02.014.

    Article  Google Scholar 

  17. Sundar, L.S., Singh, M.K., and Sousa, A.C.M., Thermal Conductivity of Ethylene Glycol and Water Mixture Based Fe3O4 Nanofluid, Int. Comm. Heat Mass Transfer, 2013; https://doi.org/10.1016/ j.icheatmasstransfer.2013.08.026.

    Article  Google Scholar 

  18. Maxwell, J.C., A Treatise on Electricity and Magnetism, vol. 1, Oxford: Clarendon Press, 1873.

    MATH  Google Scholar 

  19. Hamilton, R.L. and Crosser, O.K., Thermal Conductivity of Heterogeneous Two-Component Systems, Ind. Eng. Chem. Fundam., 1962, vol. 1, no. 3, pp. 187–191.

    Article  Google Scholar 

  20. Kumar, D., Kumar, A., and Subudhi, S., Magnetic Field Effect on the Buoyancy-Driven Convection and Fluid Motion in Fe3O4/Water Nanofluid Filled Inside an Enclosure With Mutual Orthogonal Heaters, J. Therm. Sci. Eng. Appl., 2021, vol. 13, no. 4, p. 041021; https://doi.org/10.1115/1.4048839.

    Article  Google Scholar 

  21. Ramezanizadeh, M., Alhuyi Nazari, M., Ahmadi, M.H., Lorenzini, G., and Pop, I., A Review on the Applications of Intelligence Methods in Predicting Thermal Conductivity of Nanofluids, J. Therm. An. Calorim., 2019, vol. 138, no. 1, pp. 827–843; https://doi.org/10.1007/s10973-019-08154-3.

    Article  Google Scholar 

  22. Esfe, M.H., Wongwises, S., Naderi, A., Asadi, A., Safaei, M.R., Rostamian, H., Dahari, M., and Karimipour, A., Thermal Conductivity of Cu/TiO2–Water/EG Hybrid Nanofluid: Experimental Data and Modeling Using Artificial Neural Network and Correlation, Int. Commun. Heat Mass Transfer, 2015, vol. 66, pp. 100–104.

    Article  Google Scholar 

  23. Afrand, M., Toghraie, D., and Sina, N., Experimental Study on Thermal Conductivity of Water-Based Fe3O4 Nanofluid: Development of a New Correlation and Modeled by Artificial Neural Network, Int. Commun. Heat Mass Transfer, 2016, vol. 75, pp. 262–269.

    Article  Google Scholar 

  24. Ramezanizadeh, M. and Alhuyi Nazari, M., Modeling Thermal Conductivity of Ag/Water Nanofluid by Applying a Mathematical Correlation and Artificial Neural Network, Int. J. Low-Carbon Technol., 2019, vol. 14, no. 4, pp. 468–474.

    Article  Google Scholar 

  25. Esfe, M.H., Saedodin, S., Sina, N., Afrand, M., and Rostami, S., Designing an Artificial Neural Network to Predict Thermal Conductivity and Dynamic Viscosity of Ferromagnetic Nanofluid, Int. Comm. Heat Mass Transfer, 2015, vol.68, pp. 50–57.

    Article  Google Scholar 

  26. Esfe, M.H., Ahangar, M.R.H., Rejvani, M., Toghraie, D., and Hajmohammad, M.H., Designing an Artificial Neural Network to Predict Dynamic Viscosity of Aqueous Nanofluid of TiO2 Using Experimental Data, Int. Comm. Heat Mass Transfer, 2016, vol. 75, pp. 192–196.

    Article  Google Scholar 

  27. Ahmadi, M.H., Ahmadi, M.A., Nazari, M.A., Mahian, O., and Ghasempour, R., A Proposed Model to Predict Thermal Conductivity Ratio of Al2O3/EG Nanofluid by Applying Least Squares Support Vector Machine (LSSVM) and Genetic Algorithm as a Connectionist Approach, J. Therm. An. Calorim., 2019, vol. 135, no. 1, pp. 271–281; https://doi.org/10.1007/s10973-018-7035-z.

    Article  Google Scholar 

  28. Shahsavar, A., Bagherzadeh, S.A., Mahmoudi, B., Hajizadeh, A., Afrand, M., and Nguyen, T.K., Robust Weighted Least Squares Support Vector Regression Algorithm to Estimate the Nanofluid Thermal Properties of Water/Graphene Oxide–Silicon Carbide Mixture, Phys. A: Stat. Mech. Appl., 2019, vol. 525, pp. 1418–1428.

    Article  ADS  MathSciNet  MATH  Google Scholar 

  29. Sengers, J.V. and Watson, J.T.R., Improved International Formulations for the Viscosity and Thermal Conductivity of Water Substance, J. Phys. Chem. Ref. Data, 1986, vol. 15, no. 4, pp. 1291–1314; https://doi.org/10.1063/1.555763.

    Article  ADS  Google Scholar 

  30. Popiel, C.O. and Wojtkowiak, J., Simple Formulas for Thermophysical Properties of Liquid Water for Heat Transfer Calculations (from 0°C to 150°C), Heat Transfer Eng., 1998, vol. 19, no. 3, pp. 87–101; https://doi.org/10.1080/01457639808939929.

    Article  ADS  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, NIT Uttarakhand, Uttarakhand, India

    D. Kumar

  2. Department of Mechanical Engineering, IIT Bombay, Maharashtra, India

    A. Kumar

  3. Department of Mechanical and Industrial Engineering, IIT Roorkee, Uttarakhand, India

    S. Subudhi

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  1. D. Kumar
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Correspondence to D. Kumar.

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Kumar, D., Kumar, A. & Subudhi, S. Thermal Behavior of Magnetite Nanofluid under Magnetic Field: An Experimental Study and Development of Predictive Model to Predict Thermal Conductivity. J. Engin. Thermophys. 32, 100–116 (2023). https://doi.org/10.1134/S1810232823010095

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