Viscosity of nanofluids based on an artificial intelligence model
| dc.contributor.author | Mehrabi, M. | |
| dc.contributor.author | Sharifpur, Mohsen | |
| dc.contributor.author | Meyer, Josua P. | |
| dc.contributor.email | josua.meyer@up.ac.za | en_US |
| dc.date.accessioned | 2013-09-19T14:16:22Z | |
| dc.date.available | 2013-09-19T14:16:22Z | |
| dc.date.issued | 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 | http://www.elsevier.com/locate/ichmt | 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.identifier.uri | http://hdl.handle.net/2263/31763 | |
| 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 |