Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3 – Cu nanoparticles dispersed in ethylene glycol

Yeping Peng, Amir Parsian, Hossein Khodadadi, Mohammad Akbari, Kamal Ghani, Marjan Goodarzi and Quang-Vu Bach

Physica A: Statistical Mechanics and its Applications, 2020, vol. 549, issue C

Abstract: An artificial neural network (ANN) approach is used to determine the thermal conductivity of Al2O3 – Cu / EG with an equal volume (50:50). For this purpose, a mixture of Al2O3 and Cu (50:50) nanoparticles are added in to EG at various concentrations of 0.125 to 2.0 at T=25 to T=50 °C. The method of two-step approach is applied to add nanoparticles through the base fluid. Moreover, the feedforward multilayer perceptron of NN is examined to simulate the thermal conduction coefficient of Al2O3 – Cu nanofluid. So that, more than thirty six measured points are achieved through the experiments; while twenty five ones are chosen for ANN and eleven remained ones are applied to validate the network. It is seen that the ANN proposed approach can present the thermal conduction coefficient of hybrid nanofluids with suitable accuracy and good agreement with those of available empirical data.

Keywords: Artificial neural network; Conduction coefficient; Nanoparticles; Optimal network topology (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:549:y:2020:i:c:s0378437119322228

DOI: 10.1016/j.physa.2019.124015

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