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Information Journal Paper

Title

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

Pages

  233-241

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.

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    APA: Copy

    HOJJAT, MOHAMMAD. (2017). Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm. JOURNAL OF PARTICLE SCIENC AND TECHNOLOGY, 3(4), 233-241. SID. https://sid.ir/paper/355022/en

    Vancouver: Copy

    HOJJAT MOHAMMAD. Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm. JOURNAL OF PARTICLE SCIENC AND TECHNOLOGY[Internet]. 2017;3(4):233-241. Available from: https://sid.ir/paper/355022/en

    IEEE: Copy

    MOHAMMAD HOJJAT, “Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm,” JOURNAL OF PARTICLE SCIENC AND TECHNOLOGY, vol. 3, no. 4, pp. 233–241, 2017, [Online]. Available: https://sid.ir/paper/355022/en

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