A neural network approach to estimate non-Newtonian behavior of nanofluid phase change material containing mesoporous silica particles

Document Type : Original Article

Author

Department of Mechanical Engineering, Shahreza Campus, University of Isfahan, Iran

Abstract

Neural networks are powerful tools for evaluating the thermophysical characteristics of nanofluids to reduce the cost and time of experiments. Dynamic viscosity is an important property in nanofluids that usually needs to be accurately computed in heat transfer and nanofluid flow problems. In this paper, the rheological properties of nanofluid phase change material containing mesoporous silica nanoparticles are predicted by the artificial neural networks (ANNs) method based on the experimental database reported in literature. Experimental inputs include nanoparticle mass fractions (0-5 wt.%), temperatures (35-55℃), and shear rates (10-200 s-1), and targets include dynamic viscosities and shear stresses. A multilayer perceptron feedforward neural network with Levenberg-Marquardt back-propagation training algorithm is utilized to predict rheological properties. The optimal network architecture consists of 22 neurons in the hidden layer based on the minimum mean square error (MSE). The results showed that the developed ANN has an MSE of 6.67×10-4 and 6.55×10-3 for the training and test dataset, respectively. The predicted dynamic viscosity and shear stress also have the maximum relative error of 6.26% and 0.418%, respectively.

Keywords

References

  1. Adibi, T., Razavi, S.E., Adibi, O., “A Characteristic-based Numerical Simulation of Water-titanium Dioxide Nano-fluid in Closed Domains”, International Journal of Engineering, Transactions A: Basics,Vol. 33, No.1, (2020), 158-163. DOI: 10.5829/IJE.2020.33.01A.18   
  2. Zhong, D., Zhong, H., Wen, T., “Investigation on the thermal properties, heat transfer and flow performance of a highly self-dispersion TiO2 nanofluid in a multiport mini channel”, International Communications in Heat and Mass Transfer, Vol. 117, (2020), 104783. DOI:10.1016/j.icheatmasstransfer.2020.104783.
  3. Kundan, L., AJAY, K. “Performance Evaluation of Nanofluid (Al2O3/H2O-C2H6O2) Based Parabolic Solar Collector Using Both Experimental and CFD Techniques” International Journal of Engineering, Transactions A: Basics, Vol. 29, No.4, (2016), 572-580. doi: 10.5829/idosi.ije.2016.29.04a.17
  4. Akbarzade, S., Sedighi, K., Farhadi, M., ebrahimi, M. “Experimental Investigation of Force Convection Heat Transfer in a Car Radiator Filled with SiO2-Water Nanofluid”, International Journal of Engineering, Transactions B: Applications, Vol. 27, No.2, (2014), 333-340. doi: 10.5829/idosi.ije.2014.27.02b.17
  5. Zamanian, M., “Factor Effect Estimation in the Convective Heat Transfer Coefficient Enhancement of Al2O3/EG Nanofluid in a Double-pipe Heat Exchanger”, International Journal of Engineering, Transactions B: Applications, Vol.26, No.8, (2013), 837-844. doi: 10.5829/idosi.ije.2013.26.08b.05
  6. Kheiri, M., Davarnejad, R. “Numerical Comparison of Turbulent Heat Transfer and Flow Characteristics of SiO2/Water Nanofluid within Helically Corrugated Tubes and Plain Tube”, International Journal of Engineering, Transactions A: Basics, Vol.28, No.10, (2015),1408-1414. doi: 10.5829/idosi.ije.2015.28.10a.02
  7. Minakov, A.V, Rudyak, V.Y., Pryazhnikov, M.I., “Systematic Experimental Study of the Viscosity of Nanofluids”, Heat Transfer Engineering, (2020) DOI: 10.1080/01457632.2020.1766250.
  8. Garoosi, F., “Presenting two new empirical models for calculating the effective dynamic viscosity and thermal conductivity of nanofluids”, Powder Technology, Vol. 366, (2020), 788-820. DOI:10.1016/j.powtec.2020.03.032.
  9. Bardool, R., Bakhtyari, A., Esmaeilzadeh, F., Wang, X., “Nanofluid viscosity modeling based on the friction theory”, Journal of Molecular Liquids, Vol. 286, (2019), 110923. DOI:10.1016/j.molliq.2019.110923.
  10. Motahar, S., Bagheri-Esfeh, H., “Artificial neural network based assessment of grid-connected photovoltaic thermal systems in heating dominated regions of Iran”, Sustainable Energy Technologies and Assessments, Vol.39, (2020), 100694. DOI:10.1016/j.seta.2020.100694.
  11. Ramezanizadeh, M., Ahmadi, M.H., Nazari, M.A., Sadeghzadeh, M., Chen, L., “A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids”, Renewable and Sustainable Energy Reviews, Vol.114, (2019), 109345. DOI: 10.1016/j.rser.2019.109345.
  12. Toghraie, D., Sina, N., Adavoodi Jolfaei, N., Hajian, M., Afrand, M., “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, Vol.534, (2019), 122142. DOI: 10.1016/j.physa.2019.122142.
  13. Ahmadi, M.H., Sadeghzadeh, M., Maddah, H., Solouk, A., Kumar, R., Chau, K.W., “Precise smart model for estimating dynamic viscosity of SiO2/ethylene glycol–water nanofluid”, Engineering Applications of Computational Fluid Mechanics, Vol.13, No.1, 1095-1105. DOI: 10.1080/19942060.2019.1668303
  14. Ali, A., Ilyas S., Garg, S. et al. “Dynamic viscosity of Titania nanotubes dispersions in ethylene glycol/water-based nanofluids: Experimental evaluation and predictions from empirical correlation and artificial neural network”. International Communications in Heat and Mass Transfer, Vol.118, (2020), 118:104882. DOI:10.1016/j.icheatmasstransfer.2020.104882.
  15. Chen, Z., Zarenezhad Ashkezari, A., Tlili, I., “Applying artificial neural network and curve fitting method to predict the viscosity of SAE50/MWCNTs-TiO2 hybrid nanolubricant”, Physica A: Statistical Mechanics and its Applications, Vol.549, (2020), 123946. DOI: 10.1016/j.physa.2019.123946.
  16. Hemmat Esfe, M., Reiszadeh, M., Esfandeh, S., Afrand, M., “Optimization of MWCNTs (10%) – Al2O3 (90%)/5W50 nanofluid viscosity using experimental data and artificial neural network”, Physica A: Statistical Mechanics and its Applications, Vol.512, (2018), 731-744. DOI: 10.1016/j.physa.2018.07.040.
  17. Ansari, H.R., Zarei, M.J., Sabbaghi, S., Keshavarz, P., “A new comprehensive model for relative viscosity of various nanofluids using feed-forward back-propagation MLP neural networks”, International Communications in Heat and Mass Transfer, Vol.91, (2018), 158-164. DOI:10.1016/j.icheatmasstransfer.2017.12.012.
  18. Toghraie, D., Aghahadi, M.H., Sina, N. et al. “Application of Artificial Neural Networks (ANNs) for Predicting the Viscosity of Tungsten Oxide (WO3)-MWCNTs/Engine Oil Hybrid Nanofluid”, International Journal of Thermophysics, Vol.41, No.163, (2020). DOI: 10.1007/s10765-020-02749-x.
  19. Motahar, S., Nikkam, N., Alemrajabi, A., Khodabandeh, R., Toprak, M., Muhammed, M., “A novel phase change material containing mesoporous silica nanoparticles for thermal storage: A study on thermal conductivity and viscosity”, International Communications in Heat and Mass Transfer, Vol.56, 114-120. DOI:10.1016/j.icheatmasstransfer.2014.06.005.
  20. Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesús, O., Neural Network Design, 2nd Edition, 2014, available  April 29 2021, https://hagan.okstate.edu/NNDesign.pdf  
  21. Motahar, S., Jahangiri, M., “Transient heat transfer analysis of a phase change material heat sink using experimental data and artificial neural network”, Applied Thermal Engineering, Vol.167, (2020), 114817. DOI:10.1016/j.applthermaleng.2019.114817.
  22. Marquardt, D.W., “An algorithm for least-squares estimation of nonlinear parameters”, Journal of the Society for Industrial and Applied Mathematics, Vol.11, (1963), 431–441. DOI: https://doi.org/10.1137/0111030
  23. Oraon, M., Sharma, V. “Predicting Force in Single Point Incremental Forming by Using Artificial Neural Network”, International Journal of Engineering, Transactions A: Basics, Vol.31, No.1, (2018), 88-95. doi: 10.5829/ije.2018.31.01a.13
  24. Motahar, S., “Experimental study and ANN-based prediction of melting heat transfer in a uniform heat flux PCM enclosure”, Journal of Energy Storage, Vol. 30, (2020), 101535. DOI:10.1016/j.est.2020.101535.
  25. Motahar, S., Sadri, S., “Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles”, International Journal of Energy Research, (2021), 1– 18. DOI:10.1002/er.6785
  26. Abu-Hamdeh, N.H., Golmohammadzadeh, A., Karimipour, A., “Performing regression-based methods on viscosity of nano-enhanced PCM-Using ANN and RSM”, Journal of Materials Research and Technology, Vol. 10, (2021), 1184-1194, https://doi.org/10.1016/j.jmrt.2020.12.040
International Journal of Engineering
Volume 34, Issue 8
TRANSACTIONS B: Applications
August 2021
Pages 1974-1981

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