Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina-water nanofluids
| 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 | 2012-09-14T14:38:16Z | |
| dc.date.available | 2012-09-14T14:38:16Z | |
| dc.date.issued | 2012-08 | |
| dc.description.abstract | By using an FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network as well as experimental data, two models were established in order to predict the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In these models, the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. The empirical data were divided into train and test sections for developing the models. Therefore, they were instructed by 80% of the experimental data and the remaining data (20%) were considered for benchmarking. The results, which were obtained by the proposed FCM-based Neuro-Fuzzy Inference System (FCMANFIS) and Genetic Algorithm-Polynomial Neural Network (GA-PNN) models, were provided and discussed in detail. | en_US |
| dc.description.librarian | ai2012 | en |
| dc.description.uri | http://www.elsevier.com/locate/ichmt | en_US |
| dc.identifier.citation | M. Mehrabi, M. Sharifpur & J.P. Meyer, Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina-water nanofluids, International Communications in Heat and Mass Transfer, vol. 39, no. 7, pp. 971-977 (2012), doi: 10.1016/j.icheatmasstransfer.2012.05.017 | en_US |
| dc.identifier.issn | 0735-1933 (print) | |
| dc.identifier.issn | 1879-0178 (online) | |
| dc.identifier.other | 10.1016/j.icheatmasstransfer.2012.05.017 | |
| dc.identifier.uri | http://hdl.handle.net/2263/19784 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.rights | © 2012 Elsevier. 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 39, issue 7, Augustus 2012, doi: 10.1016/j.icheatmasstransfer.2012.05.017. | en_US |
| dc.subject | Thermal conductivity ratio | en_US |
| dc.subject | FCM-based Neuro-Fuzzy Inference System (FCM-ANFIS) | en_US |
| dc.subject | Genetic Algorithm-Polynomial Neural Network (GA-PNN) | en_US |
| dc.subject | Group Method of Data Handling (GMDH) | en_US |
| dc.subject.lcsh | Nanofluids | en |
| dc.subject.lcsh | Nanofluids -- Thermal conductivity | en |
| dc.title | Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina-water nanofluids | en_US |
| dc.type | Postprint Article | en_US |