Modeling and optimization of thermal conductivity and viscosity of MnFe2O4 nanofluid under magnetic field using an ANN View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Mohammad Amani, Pouria Amani, Alibakhsh Kasaeian, Omid Mahian, Ioan Pop, Somchai Wongwises

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 MnFe2O4 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. More... »

PAGES

17369

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-017-17444-5

DOI

http://dx.doi.org/10.1038/s41598-017-17444-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1099597348

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29234090


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48 schema:description 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<sub>2</sub>O<sub>4</sub> 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.
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