Viscosity of nanofluids based on an artificial intelligence model

Show simple item record Mehrabi, M. Sharifpur, Mohsen Meyer, Josua P. 2013-09-19T14:16:22Z 2013-09-19T14:16:22Z 2013-04
dc.description.abstract By using an FCM-based Adaptive neuro-fuzzy inference system (FCM-ANFIS) and a set of experimental data, models were developed to predict the effective viscosity of nanofluids. The effective viscosity was selected as the target parameter, and the volume concentration, temperature and size of the nanoparticles were considered as the input (design) parameters. To model the viscosity, experimental data from literature were divided into two sets: a train and a test data set. The model was instructed by the train set and the results were compared with the experimental data set. The predicted viscosities were compared with experimental data for four nanofluids, which were Al2O3, CuO, TiO2 and SiO2, and with water as base fluid. The viscosities were also compared with several of themost cited correlations in literature. The results, which were obtained by the proposed FCM-ANFIS model, in general compared very well with the experimental measurement. en_US
dc.description.librarian hb2013 en_US
dc.description.sponsorship NRF, Stellenbosch University/University of Pretoria Solar Hub, CSIR, EEDSM Hub and NAC. en_US
dc.description.uri en_US
dc.identifier.citation Mehrabi, M, Sharifpur, M, & Meyer, JP 2013, 'Viscosity of nanofluids based on an artificial intelligence model', International Communications in Heat and Mass Transfer , vol. 43, no. 4, pp. 16-21. en_US
dc.identifier.issn 0735-1933 (print)
dc.identifier.issn 1879-0178 (online)
dc.identifier.other 10.1016/j.icheatmasstransfer.2013.02.008
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.rights © 2013 Elsevier Ltd. All rights reserved.Notice : this is the author’s version of a work that was accepted for publication in International Communications in Heat and Mass Transfer .Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Communications in Heat and Mass Transfer, vol. 43, no. 4, 2013. doi.: 10.1016/j.icheatmasstransfer.2013.02.008 en_US
dc.subject Nanofluid en_US
dc.subject Effective viscosity en_US
dc.subject FCM-based adaptive neuro-fuzzy inference en_US
dc.subject System (FCM-ANFIS) en_US
dc.subject Particle size en_US
dc.subject Volume concentration en_US
dc.subject Temperature en_US
dc.title Viscosity of nanofluids based on an artificial intelligence model en_US
dc.type Postprint Article en_US

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