ORCID Profile
0000-0001-8120-9946
Current Organisation
Arak University of Technology
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Publisher: Elsevier BV
Date: 02-2019
Publisher: Springer Science and Business Media LLC
Date: 24-04-2018
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 10-2017
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 10-2018
Publisher: Elsevier BV
Date: 10-2018
Publisher: Elsevier BV
Date: 07-2017
Publisher: Springer Science and Business Media LLC
Date: 07-04-2017
Publisher: Elsevier BV
Date: 05-2017
Publisher: Elsevier BV
Date: 04-2017
Publisher: Elsevier BV
Date: 08-2017
Publisher: Elsevier BV
Date: 06-2018
Publisher: Elsevier BV
Date: 03-2017
Publisher: Elsevier BV
Date: 11-2018
Publisher: Springer Science and Business Media LLC
Date: 12-12-2017
DOI: 10.1038/S41598-017-17444-5
Abstract: This research investigates the applicability of an ANN and genetic algorithms for modeling and multiobjective optimization of the thermal conductivity and viscosity of water-based spinel-type MnFe 2 O 4 nanofluid. Levenberg-Marquardt, quasi-Newton, and resilient backpropagation methods are employed to train the ANN. The support vector machine (SVM) method is also presented for comparative purposes. Experimental results demonstrate the efficacy of the developed ANN with the LM-BR training algorithm and the 3-10-10-2 structure for the prediction of the thermophysical properties of nanofluids in terms of the significantly superior accuracy compared to developing the correlation and employing SVM regression. Moreover, the genetic algorithm is implemented to determine the optimal conditions, i.e., maximum thermal conductivity and minimum nanofluid viscosity, based on the developed ANN.
Location: Iran (Islamic Republic of)
No related grants have been discovered for Mohammad Amani.