Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM)

  • Malik Muhammad Hafeezullah Institute of Computer Science and Mathematics, University of Sindh, Jamshoro, Pakistan; Department of Mathematics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan
  • Abdul Rafay Department of Physics, University of Karachi, Karachi, Pakistan
  • Ghulam Mustafa Department of Physics, NED University of Engineering and Technology, Karachi, Pakistan
  • Muhammad Khalid Department of Physics, University of Karachi, Karachi, Pakistan
  • Zubair Ahmed Kalhoro Department of Mathematics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan
  • Abdul Wasim Shaikh Institute of Computer Science and Mathematics, University of Sindh, Jamshoro, Pakistan
  • Ahmed Ali Rajput Department of Physics, University of Karachi, Karachi, Pakistan
DOI: https://doi.org/10.26565/2312-4334-2023-3-54
Keywords: Cobalt Ferrite, Nanofluids, Viscosity, Solid volume fraction, ANN, RSM

Abstract

Heat transmission by ordinary fluids such as pure water, oil, and ethylene glycol is inefficient due to their low viscosity. To boost the efficiency of conventional fluids, very small percent of nanoparticles are added to the base fluids to prepare nanofluid. The impact of changing in viscosity can be used to investigate the rheological properties of nanofluids. In this paper, (CoFe2O4)/engine oil based nanofluids were prepared using two steps standard methodology. In first step, CoFe2O4 (CF) were synthesized using the sol-gel wet chemical process. The crystalline structure and morphology were confirmed using X-Ray diffraction analysis (XRD) and scanning electron microscopy (SEM), respectively. In second step, the standard procedure was adapted by taking several solid volume fractions of CF as Ø = 0, 0.25, 0.50, 0.75, and 1.0 %. Such percent of concentrations were dispersed in appropriate volume of engine oil using the ultrasonication for 5 h. After date, the viscosity of prepared five different nanofluids were determined at temperatures ranging from 40 to 80 °C. According to the findings, the viscosity of nanofluids (µnf) decreased as temperature increased while increased when the volume percentage of nanofluids Ø raised. Furthermore, total 25 experimental observations were considered to predict viscosity using an artificial neural network (ANN) and response surface methodology (RSM). The algorithm for building the ideal ANN architecture has been recommended in order to predict the fluid velocity of the CF/SAE-50 oil based nanofluid using MATLAB software. In order to determine the correctness of the predicted model, the mean square error (MSE) was calculated 0.0136.

Downloads

Download data is not yet available.

References

Kasaeian, A., et al., A review on the applications of nanofluids in solar energy systems. 2015. 43: p. 584-598.

Nkurikiyimfura, I., et al., Heat transfer enhancement by magnetic nanofluids—a review. 2013. 21: p. 548-561.

A[1] A. Kasaeian, A.T. Eshghi, and M. Sameti, "A review on the applications of nanofluids in solar energy systems," Renewable and Sustainable Energy Reviews, 43, 584 598 (2015). https://doi.org/10.1016/j.rser.2014.11.020

I. Nkurikiyimfura, Y. Wang, and Z. Pan, "Heat transfer enhancement by magnetic nanofluids - a review," Renewable and Sustainable Energy Reviews, 21, 548-561 (2013). https://doi.org/10.1016/j.rser.2012.12.039

S. Angayarkanni, and J. Philip, "Review on thermal properties of nanofluids: Recent developments," Advances in Colloid and Interface Science, 225: 146 176. 2015. https://doi.org/10.1016/j.cis.2015.08.014

M. Raja, R. Vijayan, P. Dineshkumar, and M. Venkatesan, "Review on nanofluids characterization, heat transfer characteristics and applications," Renewable and Sustainable Energy Reviews, 64, 163-173 (2016). https://doi.org/10.1016/j.rser.2016.05.079

S.K. Das, S.U. Choi, and H.E. Patel, "Heat transfer in nanofluids—a review," Heat transfer engineering, 27(10), 3-19 (2006). https://doi.org/10.1080/01457630600904593

T. Maré, S. Halelfadl, S. Van Vaerenbergh, and P. Estellé, "Unexpected sharp peak in thermal conductivity of carbon nanotubes water-based nanofluids," International Communications in Heat and Mass Transfer, 66, 80-83 (2015). https://doi.org/10.1016/j.icheatmasstransfer.2015.05.013

Y.L. Zhai, G.D. Xia, X.F. Liu, and Y.F. Li, "Heat transfer enhancement of Al2O3-H2O nanofluids flowing through a micro heat sink with complex structure," 66, 158-166 (2015). http://dx.doi.org/10.1016%2Fj.icheatmasstransfer.2015.05.025

M.A. Sheremet, I. Pop, and M.M. Rahman, "Three-dimensional natural convection in a porous enclosure filled with a nanofluid using Buongiorno’s mathematical model," International Journal of Heat and Mass Transfer, 82, 396-405 (2015). https://doi.org/10.1016/j.ijheatmasstransfer.2014.11.066

Jang, S.P. and S.U.S. Choi, "Role of Brownian motion in the enhanced thermal conductivity of nanofluids," Appl. Phys. Lett. 84(21), 4316 4318. (2004). https://doi.org/10.1063/1.1756684

S.K. Das, and S.U.S. Choi, "A review of heat transfer in nanofluids," Advances in Heat transfer, 41, 81-197 (2009). https://doi.org/10.1016/S0065-2717(08)41002-X

D.S. Udawattha, M. Narayana, and U.P.L. Wijayarathne, "Predicting the effective viscosity of nanofluids based on the rheology of suspensions of solid particles," 31(3), 412-426 (2019). https://doi.org/10.1016/j.jksus.2017.09.016

Y. Yang, Z.G.Z., E.A. Grulke, W.B. Anderson, and G. Wu, "Heat transfer properties of nanoparticle-in-fluid dispersions (nanofluids) in laminar flow," 48(6), 1107-1116 (2005). https://doi.org/10.1016/j.ijheatmasstransfer.2004.09.038

M. Gupta, R. Kumar, N. Arora, S. Kumar, and N. Dilbagi, "Forced convective heat transfer of MWCNT/water nanofluid under constant heat flux: an experimental investigation," Arabian Journal for Science and Engineering, 41(2), 599-609 (2016). https://doi.org/10.1007/s13369-015-1699-5

W.-Q. Lu, and and Q.-M. Fan, "Study for the particle's scale effect on some thermophysical properties of nanofluids by a simplified molecular dynamics method," 32(4), 282-289 (2008). https://doi.org/10.1016/j.enganabound.2007.10.006

K.V. Wong, and O. De Leon, "Applications of nanofluids: current and future," Advances in mechanical engineering, 2, 519659 (2010). https://doi.org/10.1155/2010/519659

A. Patra, M.K. Nayak, and A. Misra, "Viscosity of nanofluids-A Review," International Journal of Thermofluid Science and Technology, 7(2), 070202 (2020). https://doi.org/10.36963/IJTST.2020070202

M.J. Pastoriza-Gallego, C. Casanova, J.L. Legido, and M.M. Piñeiro, "CuO in water nanofluid: influence of particle size and polydispersity on volumetric behaviour and viscosity," 300(1-2), 188-196 (2011). https://doi.org/10.1016/j.fluid.2010.10.015

T.X. Phuoc, M. Massoudi, and R.-H. Chen, "Viscosity and thermal conductivity of nanofluids containing multi-walled carbon nanotubes stabilized by chitosan," International Journal of Thermal Sciences, 50(1), 12-18 (2011). https://doi.org/10.1016/j.ijthermalsci.2010.09.008

P.K. Namburu, D.K. Das, K.M. Tanguturi, and R.S. Vajjha, "Numerical study of turbulent flow and heat transfer characteristics of nanofluids considering variable properties,' 48(2), 290-302 (2009). https://doi.org/10.1016/j.ijthermalsci.2008.01.001

J. Li, C. Zhou, G. Wang, and D. Zhao, "Study on rheological behavior of polypropylene/clay nanocomposites," Journal of applied polymer science, 89(13), 3609-3617 (2003). https://doi.org/10.1002/app.12643

M. Gupta, V. Singh, R. Kumar, and Z. Said, "A review on thermophysical properties of nanofluids and heat transfer applications," Renewable and Sustainable Energy Reviews, 74, 638-670 (2017). https://doi.org/10.1016/j.rser.2017.02.073

R. Saidur, K. Leong, and H.A. Mohammed, "A review on applications and challenges of nanofluids," Renewable and sustainable energy reviews, 15(3), 1646-1668 (2011). https://doi.org/10.1016/j.rser.2010.11.035

H. Karimi, F. Yousefi, and M.R. Rahimi, "Correlation of viscosity in nanofluids using genetic algorithm-neural network (GA-NN)," Heat and mass transfer, 47(11), 1417-1425 (2011). http://dx.doi.org/10.1007%2Fs00231-011-0802-z

M. Gholizadeh, M. Jamei, I. Ahmadianfar, and R. Pourrajab, "Prediction of nanofluids viscosity using random forest (RF) approach," Chemometrics and Intelligent Laboratory Systems, 201, 104010 (2020). https://doi.org/10.1016/j.chemolab.2020.104010

P.K. Kanti, K.V. Sharma, K.M. Yashawantha, and S. Dmk, "Experimental determination for viscosity of fly ash nanofluid and fly ash-Cu hybrid nanofluid: Prediction and optimization using artificial intelligent techniques," Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1-20 (2021). https://doi.org/10.1080/15567036.2021.1877374

A.A.A.A. Alrashed, M.S. Gharibdousti, M. Goodarzi, L.R. de Oliveira, M.R. Safaei, and E.P.B. Filho, "Effects on thermophysical properties of carbon based nanofluids: experimental data, modelling using regression, ANFIS and ANN," International Journal of Heat and Mass Transfer, 125, 920-932 (2018). https://doi.org/10.1016/j.ijheatmasstransfer.2018.04.142

M.H. Ahmadi, B. Mohseni-Gharyehsafa, M. Farzaneh-Gord, R.D. Jilte, and R. Kumar, "Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms," Engineering Applications of Computational Fluid Mechanics, 13(1), 220-228 (2019). https://doi.org/10.1080/19942060.2019.1571442

M.H. Esfe, S. Saedodin, M. Bahiraei, D. Toghraie, O. Mahian, and S. Wongwises, "Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network," Journal of Thermal Analysis and Calorimetry, 118(1), 287-294 (2014). https://doi.org/10.1007/s10973-014-4002-1

N. Zhao, and Z. Li, "Experiment and artificial neural network prediction of thermal conductivity and viscosity for alumina-water nanofluids," Materials, 10(5), 552 (2017). https://doi.org/10.3390/ma10050552

M. Vakili, S. Khosrojerdi, P. Aghajannezhad, and M. Yahyaei, "A hybrid artificial neural network-genetic algorithm modeling approach for viscosity estimation of graphene nanoplatelets nanofluid using experimental data," International Communications in Heat and Mass Transfer, 82, 40-48 (2017). https://doi.org/10.1016/j.icheatmasstransfer.2017.02.003

M. Vakili, M. Karami, S. Delfani, and S. Khosrojerdi, "Experimental investigation and modeling of thermal radiative properties of f-CNTs nanofluid by artificial neural network with Levenberg–Marquardt algorithm," International Communications in Heat and Mass Transfer, 78, 224-230 (2016). https://doi.org/10.1016/j.icheatmasstransfer.2016.09.011

A.M Ghanadi, A.H. Nasab, D. Bastani, and A.A.S. Kordi, "The effect of nanoparticles on the mass transfer in liquid–liquid extraction," Chemical Engineering Communications, 202(5), 600-605 (2015). https://doi.org/10.1080/00986445.2013.858037

M. Abdollahi-Moghaddam, K. Motahari, and A. Rezaei, "Performance characteristics of low concentrations of CuO/water nanofluids flowing through horizontal tube for energy efficiency purposes; an experimental study and ANN modeling," Journal of Molecular Liquids, 271, 342-352 (2018). https://doi.org/10.1016/j.molliq.2018.08.149

M.H. Esfe, and S.M.S. Tilebon, "Statistical and artificial based optimization on thermo-physical properties of an oil-based hybrid nanofluid using NSGA-II and RSM," Physica A: Statistical Mechanics and its Applications, 537, 122126 (2020). https://doi.org/10.1016/j.physa.2019.122126

M.H. Esfe, M. Goodarzi, and S. Esfandeh, "Experimental investigation of c(75–25%)/10W40 as a new nano-lubricant," The European Physical Journal Plus, 136(5), 605 (2021). https://doi.org/10.1140/epjp/s13360-021-01414-y

M.H. Esfe, and M.H. Hajmohammad, "Thermal conductivity and viscosity optimization of nanodiamond-Co3O4/EG (40: 60) aqueous nanofluid using NSGA-II coupled with RSM," Journal of Molecular Liquids, 238, 545-552 (2017). https://doi.org/10.1016/j.molliq.2017.04.056

M.H. Esfe, and S. Alidoust, "Modeling and precise prediction of thermophysical attributes of water/EG blend-based CNT nanofluids by NSGA-II using ANN and RSM," Arabian Journal for Science and Engineering, 46(7), 6423-6437 (2021).

Maqsood, K., et al., Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids. Chemosphere, 286, 131690 (2022). https://doi.org/10.1007/s13369-020-05086-1

M.H. Esfe, M.H. Kamyab, "Mathematical monitoring of agglomeration effects on thermophysical properties of water-based nanofluids using MLP and RSM," Journal of Thermal Analysis and Calorimetry, 146(2), 739-756 (2021). https://doi.org/10.1007/s10973-020-09996-y

N. Zhao, and Z. Li, "Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks," 281, 173-183 (2015). https://doi.org/10.3390/app7040409

O.A. Alawi, N.A.C. Sidik, H.W. Xian, T.H. Kean, and S.N. Kazi, "Thermal conductivity and viscosity models of metallic oxides nanofluids," 116, 1314-1325 (2018). https://doi.org/10.1016/j.ijheatmasstransfer.2017.09.133

D.-H. Chen, and X.-R. He, "Synthesis of nickel ferrite nanoparticles by sol-gel method," Materials Research Bulletin, 36(7-8), 1369-1377 (2001). https://doi.org/10.1016/S0025-5408(01)00620-1

M. Salavati-Niasari, F.Davar, M. Mazaheri, and M. Shaterian, "Preparation of cobalt nanoparticles from [bis (salicylidene) cobalt (II)]–oleylamine complex by thermal decomposition," Journal of Magnetism and Magnetic Materials, 320(3-4), 575-578 (2008). https://doi.org/10.1016/j.jmmm.2007.07.020

M. Scimeca, S. Bischetti, H.K. Lamsira, R. Bonfiglio, and E. Bonanno, "Energy Dispersive X-ray (EDX) microanalysis: A powerful tool in biomedical research and diagnosis," European journal of histochemistry, 62(1), (2018). https://doi.org/10.4081/ejh.2018.2841

W. Zhou, R. Apkarian, Z.L. Wang, and D. Joy, "Fundamentals of scanning electron microscopy (SEM)," in: Scanning microscopy for nanotechnology, edited by D. Joy, and Z.L. Wang, (Springer, NY, 2006), pp. 1-40.

Published
2023-09-04
Cited
How to Cite
Hafeezullah, M. M., Rafay, A., Mustafa, G., Khalid, M., Kalhoro, Z. A., Shaikh, A. W., & Rajput, A. A. (2023). Prediction of Viscosity of Cobalt Ferrite/SAE50 Engine Oil based Nanofluids using well Trained Artificial Neutral Network (ANN) and Response Surface Methodology (RSM). East European Journal of Physics, (3), 479-489. https://doi.org/10.26565/2312-4334-2023-3-54
Issue
No. 3 (2023): East European Journal of Physics
Section
Original Papers

Copyright (c) 2023 Malik Muhammad Hafeezullah, Abdul Rafay, Ghulam Mustafa, Muhammad Khalid, Zubair Ahmed Kalhoro, Abdul Wasim Shaikh, Ahmed Ali Rajpu

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
    • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
    • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).