Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system

ANFIS and multiphase Mixture model, two different methods from two very distinct engineering domains: Machine Intelligence and Computational Mechanics, have been put into a good amount of use over the past few years for modeling of nanofluids heat transfer enhancement. However, not only the previous...

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Main Authors: Al Mahmud, Suaib, Khan, Mazbahur Rahman, Noor, Wazed Ibne, Ismail, Ahmad Faris, Momin, Md. Abdul, Bappy, Jamirul Habib
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Published: Taylor & Francis Inc 2023
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Online Access:http://eprints.um.edu.my/39309/
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spelling my.um.eprints.393092023-11-29T02:13:29Z http://eprints.um.edu.my/39309/ Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system Al Mahmud, Suaib Khan, Mazbahur Rahman Noor, Wazed Ibne Ismail, Ahmad Faris Momin, Md. Abdul Bappy, Jamirul Habib TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering ANFIS and multiphase Mixture model, two different methods from two very distinct engineering domains: Machine Intelligence and Computational Mechanics, have been put into a good amount of use over the past few years for modeling of nanofluids heat transfer enhancement. However, not only the previous investigations using both of these approaches suffer from the use of narrow range of nanofluid and flow properties, but also never have these two particular approaches been juxtaposed to point at a superior approach for specific flow regimes for predicting nanofluids heat transfer enhancement. In this study, water-Al2O3 nanofluid has been simulated using CFD multiphase Mixture model and ANFIS in order to assess the precision of both approaches to predict heat transfer enhancement of water-Al2O3 nanofluid for a very wide range of nanofluid configurations and flow properties, and to suggest the better approach for prediction of heat transfer enhancement for each specific flow regime. The results suggest that almost in every single case ANFIS is able to predict the heat transfer enhancement of nanofluids very efficiently with a maximum error of 0.35%, but the Mixture models' predictions deviate significantly from the experimental correlation in some cases, though for intermediate nanofluid configurations, yielded results by Mixture model could be reliable with error around 1%. Taylor & Francis Inc 2023-03-04 Article PeerReviewed Al Mahmud, Suaib and Khan, Mazbahur Rahman and Noor, Wazed Ibne and Ismail, Ahmad Faris and Momin, Md. Abdul and Bappy, Jamirul Habib (2023) Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system. Numerical Heat Transfer Part B-Fundamentals, 83 (3). pp. 120-138. ISSN 1040-7790, DOI https://doi.org/10.1080/10407790.2022.2149197 <https://doi.org/10.1080/10407790.2022.2149197>. 10.1080/10407790.2022.2149197
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
Al Mahmud, Suaib
Khan, Mazbahur Rahman
Noor, Wazed Ibne
Ismail, Ahmad Faris
Momin, Md. Abdul
Bappy, Jamirul Habib
Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system
description ANFIS and multiphase Mixture model, two different methods from two very distinct engineering domains: Machine Intelligence and Computational Mechanics, have been put into a good amount of use over the past few years for modeling of nanofluids heat transfer enhancement. However, not only the previous investigations using both of these approaches suffer from the use of narrow range of nanofluid and flow properties, but also never have these two particular approaches been juxtaposed to point at a superior approach for specific flow regimes for predicting nanofluids heat transfer enhancement. In this study, water-Al2O3 nanofluid has been simulated using CFD multiphase Mixture model and ANFIS in order to assess the precision of both approaches to predict heat transfer enhancement of water-Al2O3 nanofluid for a very wide range of nanofluid configurations and flow properties, and to suggest the better approach for prediction of heat transfer enhancement for each specific flow regime. The results suggest that almost in every single case ANFIS is able to predict the heat transfer enhancement of nanofluids very efficiently with a maximum error of 0.35%, but the Mixture models' predictions deviate significantly from the experimental correlation in some cases, though for intermediate nanofluid configurations, yielded results by Mixture model could be reliable with error around 1%.
format Article
author Al Mahmud, Suaib
Khan, Mazbahur Rahman
Noor, Wazed Ibne
Ismail, Ahmad Faris
Momin, Md. Abdul
Bappy, Jamirul Habib
author_facet Al Mahmud, Suaib
Khan, Mazbahur Rahman
Noor, Wazed Ibne
Ismail, Ahmad Faris
Momin, Md. Abdul
Bappy, Jamirul Habib
author_sort Al Mahmud, Suaib
title Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system
title_short Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system
title_full Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system
title_fullStr Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system
title_full_unstemmed Turbulent convective heat transfer enhancement modeling of water-Al2O3 nanofluid using CFD mixture model and adaptive neural fuzzy inference system
title_sort turbulent convective heat transfer enhancement modeling of water-al2o3 nanofluid using cfd mixture model and adaptive neural fuzzy inference system
publisher Taylor & Francis Inc
publishDate 2023
url http://eprints.um.edu.my/39309/
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score 13.18916