Validation and Optimization of Thermophysical Properties for Thermal Conductivity and Viscosity of Nanofluid Engine Oil using Neural Network




In this study, the thermophysical properties of thermal conductivity and viscosity of a motor oil nanofluid were investigated using experimental data and artificial neural network. NSGA II optimization algorithm was used to maximize thermal conductivity and minimum viscosity with changes in temperature and volume fraction of nanofluids. Also, to obtain the viscosity and thermal conductivity values in terms of nanofluid temperature and volume fraction with 174 experimental data, neural network modeling was performed. Input data include temperature and volume fraction, and output is viscosity and thermal conductivity. Various indices such as R squared and Mean Square Error (MSE) have been used to evaluate the accuracy of modeling in the prediction of viscosity and thermal conductivity of nanofluids. The coefficient of determination R squared is 0.9989 indicating acceptable agreement with the experimental data. In order to optimize and finally results as an objective function, the optimization algorithm is presented and the Parto front and its corresponding optimum points are presented where the maximum optimization results of thermal conductivity and viscosity occur at 1% volume fraction.


Multi-objective Optimization, Nanofluid, Thermal Conductivity, Artificial Neural Network, NSGAII


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<li>Lee, S. SU, S. Choi, J. Li, A. Eastman. "Measuring thermal conductivity of fluids containing oxide nanoparticles." <em>Journal Heat Transfer </em>(1999): 280-289. <a href=""></a></li>
<li>Esmaeeli, M. Pouladian, A. Monfared, S. R. Mahdavi, D. Moslemi. "A Genetic Algorithm and Neural Network Hybrid Model to Predict Lung Radiation-Induced Pneumonitis in Breast Radiotherapy (A simulation Study)." <em>Journal of Babol University of Medical Sciences</em> 16, no. 1 (2014): 77-84. <a href=""></a></li>
<li>H. Esfe, H. Hajmohammad, R. Moradi, A. A. Abbasian. "Multi-objective optimization of cost and thermal performance of double walled carbon nanotubes/water nanofluids by NSGA-II using response surface method." <em>Applied Thermal Engineering</em>. 112 (2017): 1648-1657. <a href=""></a></li>
<li>Shriram, V. Juwar. "Optimization of conditions for an enhancement of thermal conductivity and minimization of viscosity of ethylene glycol based Fe3O4 nanofluid." <em>Applied Thermal Engineering.</em> 109 (2016): 121-129. <a href=""></a></li>
<li>, Ravikanth, D. Das. "Experimental determination of thermal conductivity of three nanofluids and development of new correlations." <em>International Journal of Heat and Mass Transfer</em> 52, no. 21-22 (2009): 4675-4682. <a href=""></a></li>
<li>R. Sabour, A. Amiri. "Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate." <em>Waste management</em> 65 (2017): 54-62. <a href=""></a></li>
<li>Zhao, L. Zhiming. "Experiment and artificial neural network prediction of thermal conductivity and viscosity for alumina-water nanofluids." <em>Materials </em>10, no. 5 (2017): 552. <a href=""></a></li>
<li>Tajik, A. H. Zamzamian. "Optimization of thermal conductivity of Al2O3 nanofluid by using ANN and GRG methods." <em>International Journal of Nanoscience and Nanotechnology</em> 9, no. 4 (2013): 177-184. <a href=""></a></li>
<li>Ameer, J. Yunhee, L. Hyun-Gyu, A. Ameer, J. Kwon. "Optimization of microwave-assisted extraction of total extract, sativoside and rebaudioside-A from Stevia rebaudiana (Bertoni) leaves, using response surface methodology (RSM) and artificial neural network (ANN) modelling." <em>Food chemistry</em> 229 (2017): 198-207. <a href=""></a></li>
<li>Shang-Ming, K. Chia-Hung, C. Chun-An, L. Yung-Chuan, J. Chwen. "RSM and ANN modeling-based optimization approach for the development of ultrasound-assisted liposome encapsulation of piceid." <em>Ultrasonic sonchemistry</em> 36 (2017): 112-122. <a href=""></a></li>
<li>Ohale, F. Uzoh, O. Onukwuli. "Optimal factor evaluation for the dissolution of alumina from Azaraegbelu clay in acid solution using RSM and ANN comparative analysis." <em>south African journal of chemical engineering</em> 24 (2017): 43-54. <a href=""></a></li>
<li>Yandamuri, K. Srinivasan, S. Bhallamudi. "Multi objective optimal waste load allocation models for rivers using non dominated sorting genetic algorithm-II." <em>Journal of water resources planning and management</em> 132, no. 3 (2006): 133-143. <a href=""></a></li>
<li>Esfe, M. Hajmohammad, P. Razi, M.R. Hassani, A. A. Abbasian. "The optimization of viscosity and thermal conductivity in hybrid nanofluids prepared with magnetic nanocomposite of Nano diamond cobalt-oxide (ND-Co3O4) using NSGA-II and RSM." <em>International Communications in Heat and Mass Transfer</em> 79 (2016): 128-134. <a href=""></a></li>
<li>Xinwei, X. Xianfan, C. Stephen. "Thermal conductivity of nanoparticle-fluid mixture." <em>Journal of thermo physics and heat transfer</em> 13, no. 4 (1999): 474-480. <a href=""></a></li>
<li>Hrishikesh, D. Sarit. T. Sundararajan, A. Sreekumaran, T. Pradeep. "Thermal conductivities of naked and monolayer protected metal nanoparticle based nanofluids: Manifestation of anomalous enhancement and chemical effects." <em>Applied Physics Letters</em> 83, no. 14 (2003): 2931-2933. <a href=""></a></li>
<li>Esfe, S. Esfandeh, S. Saedodin, H. Rostamian. "Experimental evaluation, sensitivity analyzation and ANN modeling of thermal conductivity of ZnO-MWCNT/EG-water hybrid nanofluid for engineering applications." <em>Applied Thermal Engineering</em> 125 (2017): 673-685. <a href=""></a></li>
<li>Esfe, S. Saedodin, M. Mahmoodi. "Experimental studies on the convective heat transfer performance and thermophysical properties of MgO–water nanofluid under turbulent flow." <em>Experimental thermal and fluid science</em> 52 (2014): 68-78. <a href=""></a></li>
<li>Esfe, S. Saedodin, A. Naderi, A. Alirezaie, A. Karimipour, S. Wongwises, M. Goodarzi, M. Dahari. "Modeling of thermal conductivity of ZnO-EG using experimental data and ANN methods." <em>International Communications in Heat and Mass Transfer</em> 63 (2015): 35-40. <a href=""></a></li>
<li>Esfe, P. Razi, M. H. Hajmohammad, H. Rostamian, W. Sarsam, A. Abbasian, M. Dahari. "Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN." <em>International Heat and Mass Transfer</em> 82 (2017): 154-160. <a href=""></a></li>
<li>Bagherzadeh, Seyed Amin, Mohsen Tahmasebi Sulgani, Vahid Nikkhah, Mehrdad Bahrami, Arash Karimipour, and Yu Jiang. "Minimize pressure drop and maximize heat transfer coefficient by the new proposed multi-objective optimization/statistical model composed of" ANN+ Genetic Algorithm" based on empirical data of CuO/paraffin nanofluid in a pipe." Physica A Statistical Mechanics and its Applications 527 (2019): 121056. <a href=""></a></li>
<li>Pordanjani, Ahmad Hajatzadeh, Seyed Masoud Vahedi, Farhad Rikhtegar, and Somchai Wongwises. "Optimization and sensitivity analysis of magneto-hydrodynamic natural convection nanofluid flow inside a square enclosure using response surface methodology." Journal of Thermal Analysis and Calorimetry 135, no. 2 (2019): 1031-1045. <a href=""></a></li>
<li>Moslemi Petrudi, P. Fathi, and M. Rahmani, “Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology”, J. Mod. Sim. Mater., vol. 3, no. 1, pp. 1-14, Mar. 2020. <a href=""> </a></li>
<li>Petrudi, Amin Moslemi, and Ionut Cristian Scurtu. "Investigating and Modeling the Factors Affecting Thermal Optimization and Dynamic Viscosity of Water Hybrid Nanofluid/Carbon Nanotubes via MOPSO using ANN." Technium: Romanian Journal of Applied Sciences and Technology 2, no. 3 (2020): 108-114. <a href=""></a></li>






Research Article

How to Cite

A. Moslemi Petrudi and M. Rahmani, “Validation and Optimization of Thermophysical Properties for Thermal Conductivity and Viscosity of Nanofluid Engine Oil using Neural Network”, J. Mod. Sim. Mater., vol. 3, no. 1, pp. 53-60, Jun. 2020.