Hojjat, M. (2017). Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm. Journal of Particle Science & Technology, 3(4), 233-241. doi: 10.22104/jpst.2018.2677.1107

Mohammad Hojjat. "Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm". Journal of Particle Science & Technology, 3, 4, 2017, 233-241. doi: 10.22104/jpst.2018.2677.1107

Hojjat, M. (2017). 'Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm', Journal of Particle Science & Technology, 3(4), pp. 233-241. doi: 10.22104/jpst.2018.2677.1107

Hojjat, M. Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm. Journal of Particle Science & Technology, 2017; 3(4): 233-241. doi: 10.22104/jpst.2018.2677.1107

Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm

^{}Department of Chemical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran

Abstract

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer. The network was trained by a particle swarm optimization (PSO) algorithm. Nanofluid concentration, Reynolds number, and Prandtl number are input for the ANN and the nanofluid Nusselt number is its output. There exists an excellent agreement between the ANN predicted values and experimental data. The average and maximum differences between experimental data and those predicted by ANN are about 0.8 and 5.6 %, respectively. It was also found that ANN predicts the Nusselt number of nanofluids more accurately than the previously proposed correlation.

Highlights

Designing an ANN and optimized it by using a PSO algorithm to predict the Nusselt number of non-Newtonian nanofluids

Assessment of the ability of an artificial neural network to predict convective heat transfer experimental data

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