Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80799
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorAhmadi, MH-
dc.creatorMohseni-Gharyehsafa, B-
dc.creatorFarzaneh-Gord, M-
dc.creatorJilte, RD-
dc.creatorKumar, R-
dc.creatorChau, KW-
dc.date.accessioned2019-05-28T01:09:28Z-
dc.date.available2019-05-28T01:09:28Z-
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/80799-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Mohammad Hossein Ahmadi, Behnam Mohseni-Gharyehsafa, MahmoodFarzaneh-Gord, Ravindra D. Jilte, Ravinder Kumar & Kwok-wing Chau (2019) Applicability ofconnectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP,MARS and MPR algorithms, Engineering Applications of Computational Fluid Mechanics, 13:1,220-228 is available at https://dx.doi.org/10.1080/19942060.2019.1571442en_US
dc.subjectNanofluiden_US
dc.subjectDynamic viscosityen_US
dc.subjectArtificial neural networken_US
dc.subjectConcentrationen_US
dc.subjectMultivariate adaptive regression splines (MARS)en_US
dc.subjectMultivariable polynomial regression (MPR)en_US
dc.titleApplicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithmsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage220en_US
dc.identifier.epage228en_US
dc.identifier.volume13en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2019.1571442en_US
dcterms.abstractDynamic viscosity considerably affects the heat transfer and flow of fluids. Due to improved thermophysical properties of fluids containing nanostructures, these types of fluids are widely employed in thermal mediums. The nanofluid's dynamic viscosity relies on different variables including size of solid phase, concentration and temperature. In the present study, three algorithms including multivariable polynomial regression (MPR), artificial neural network-multilayer perceptron (ANN-MLP) and multivariate adaptive regression splines (MARS) are applied to model the dynamic viscosity of silver (Ag)/water nanofluid. Recently published experimental investigations are employed for data extraction. The input variables considered in the modeling process to be the most important ones are the size of particles, fluid temperature and the concentration of Ag nanoparticles in the base fluid. The R-2 values for the studied models are 0.9998, 0.9997 and 0.9996 for the ANN-MLP, MARS and MPR algorithms, respectively. In addition, based on importance analysis, the temperature is highly effective and the dominant parameter for the dynamic viscosity of the nanofluid in comparison with size and concentration.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 220-228-
dcterms.isPartOfEngineering applications of computational fluid mechanics-
dcterms.issued2019-
dc.identifier.isiWOS:000460325800001-
dc.identifier.scopus2-s2.0-85065891420-
dc.identifier.eissn1997-003Xen_US
dc.description.validate201905 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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