Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80437
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorBaghban, A-
dc.creatorJalali, A-
dc.creatorShafiee, M-
dc.creatorAhmadi, MH-
dc.creatorChau, KW-
dc.date.accessioned2019-03-26T09:17:10Z-
dc.date.available2019-03-26T09:17:10Z-
dc.identifier.issn1994-2060-
dc.identifier.urihttp://hdl.handle.net/10397/80437-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This 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 Baghban, A., Jalali, A., Shafiee, M., Ahmadi, M. H., & Chau, K. W. (2019). Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids. Engineering Applications of Computational Fluid Mechanics, 13(1), 26-39 is available at https://dx.doi.org/10.1080/19942060.2018.1542345en_US
dc.subjectNanofluiden_US
dc.subjectViscosityen_US
dc.subjectANFISen_US
dc.subjectSensitivity analysisen_US
dc.subjectCorrelationsen_US
dc.titleDeveloping an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluidsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage26-
dc.identifier.epage39-
dc.identifier.volume13-
dc.identifier.issue1-
dc.identifier.doi10.1080/19942060.2018.1542345-
dcterms.abstractNanofluid viscosity is an important physical property in convective heat transfer phenomena. However, the current theoretical models for nanofluid viscosity prediction are only applicable across a limited range. In this study, 1277 experimental data points of distinct nanofluid relative viscosity (NF-RV) were gathered from a plenary literature review. In order to create a general model, adaptive network-based fuzzy inference system (ANFIS) code was expanded based on the independent variables of temperature, nanoparticle diameter, nanofluid density, volumetric fraction, and viscosity of the base fluid. A statistical analysis of the data for training and testing (with R-2 = .99997) demonstrates the accuracy of the model. In addition, the results obtained from ANFIS are compared to similar experimental data and show absolute and maximum average relative deviations of about 0.42 and 6.45%, respectively. Comparisons with other theoretical models from previous research is used to verify the model and prove the prediction capabilities of ANFIS. Consequently, this tool can be of huge value in helping chemists and mechanical and chemical engineers - especially those who are dealing with heat transfer applications by nanofluids - by providing highly accurate predictions of NF-RVs.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 26-39-
dcterms.isPartOfEngineering applications of computational fluid mechanics-
dcterms.issued2019-
dc.identifier.isiWOS:000451223200001-
dc.identifier.eissn1997-003X-
dc.description.validate201903 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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