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Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression

The specific heat capacity of nanofluids is a fundamental thermophysical property that measures the heat storage capacity of the nanofluids. is usually determined through experimental measurement. As it is known, experimental procedures are characterised with some complexities, which include, the...

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Main Authors: Olanrewaju, Alade A., Abd Rahman, Mohd Amiruddin, Aliyu, Bagudu, Abbas, Zulkifly, Yaakob, Yazid, A. Saleh, Tawfik
Format: Article
Language:English
Published: Elsevier 2019
Online Access:http://psasir.upm.edu.my/id/eprint/81145/1/glycol.pdf
http://psasir.upm.edu.my/id/eprint/81145/
https://www.sciencedirect.com/science/article/pii/S2405844018377934#!
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spelling my.upm.eprints.811452020-09-09T07:07:06Z http://psasir.upm.edu.my/id/eprint/81145/ Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression Olanrewaju, Alade A. Abd Rahman, Mohd Amiruddin Aliyu, Bagudu Abbas, Zulkifly Yaakob, Yazid A. Saleh, Tawfik The specific heat capacity of nanofluids is a fundamental thermophysical property that measures the heat storage capacity of the nanofluids. is usually determined through experimental measurement. As it is known, experimental procedures are characterised with some complexities, which include, the challenge of preparing stable nanofluids and relatively long periods to conduct experiments. So far, two correlations have been developed to estimate the The accuracies of these models are still subject to further improvement for many nanofluid compositions. This study presents a four-input support vector regression (SVR) model hybridized with a Bayesian algorithm to predict the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. The bayesian algorithm was used to obtain the optimum SVR hyperparameters. 189 experimental data collected from published literature was used for the model development. The proposed model exhibits low average absolute relative deviation (AARD) and a high correlation coefficient (r) of 0.40 and 99.53 %, respectively. In addition, we analysed the accuracies of the existing analytical models on the considered nanofluid compositions. The model based on the thermal equilibrium between the nanoparticles and base fluid (model II) show good agreement with experimental results while the model based on simple mixing rule (model I) overestimated the specific heat capacity of the nanofluids. To further validate the superiority of the proposed technique over the existing analytical models, we compared various statistical errors for the three models. The AARD for the BSVR, model II, and model I are 0.40, 0.82 and 4.97, respectively. This clearly shows that the model developed has much better prediction accuracy than existing models in predicting the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. We believe the presented model will be important in the design of nanofluid-based applications due to its improved accuracy. Elsevier 2019-05 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81145/1/glycol.pdf Olanrewaju, Alade A. and Abd Rahman, Mohd Amiruddin and Aliyu, Bagudu and Abbas, Zulkifly and Yaakob, Yazid and A. Saleh, Tawfik (2019) Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression. Heliyon, 5 (6). art. no. e01882. pp. 1-7. ISSN 2405-8440 https://www.sciencedirect.com/science/article/pii/S2405844018377934#! 10.1016/j.heliyon.2019.e01882
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The specific heat capacity of nanofluids is a fundamental thermophysical property that measures the heat storage capacity of the nanofluids. is usually determined through experimental measurement. As it is known, experimental procedures are characterised with some complexities, which include, the challenge of preparing stable nanofluids and relatively long periods to conduct experiments. So far, two correlations have been developed to estimate the The accuracies of these models are still subject to further improvement for many nanofluid compositions. This study presents a four-input support vector regression (SVR) model hybridized with a Bayesian algorithm to predict the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. The bayesian algorithm was used to obtain the optimum SVR hyperparameters. 189 experimental data collected from published literature was used for the model development. The proposed model exhibits low average absolute relative deviation (AARD) and a high correlation coefficient (r) of 0.40 and 99.53 %, respectively. In addition, we analysed the accuracies of the existing analytical models on the considered nanofluid compositions. The model based on the thermal equilibrium between the nanoparticles and base fluid (model II) show good agreement with experimental results while the model based on simple mixing rule (model I) overestimated the specific heat capacity of the nanofluids. To further validate the superiority of the proposed technique over the existing analytical models, we compared various statistical errors for the three models. The AARD for the BSVR, model II, and model I are 0.40, 0.82 and 4.97, respectively. This clearly shows that the model developed has much better prediction accuracy than existing models in predicting the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. We believe the presented model will be important in the design of nanofluid-based applications due to its improved accuracy.
format Article
author Olanrewaju, Alade A.
Abd Rahman, Mohd Amiruddin
Aliyu, Bagudu
Abbas, Zulkifly
Yaakob, Yazid
A. Saleh, Tawfik
spellingShingle Olanrewaju, Alade A.
Abd Rahman, Mohd Amiruddin
Aliyu, Bagudu
Abbas, Zulkifly
Yaakob, Yazid
A. Saleh, Tawfik
Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression
author_facet Olanrewaju, Alade A.
Abd Rahman, Mohd Amiruddin
Aliyu, Bagudu
Abbas, Zulkifly
Yaakob, Yazid
A. Saleh, Tawfik
author_sort Olanrewaju, Alade A.
title Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression
title_short Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression
title_full Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression
title_fullStr Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression
title_full_unstemmed Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression
title_sort development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression
publisher Elsevier
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/81145/1/glycol.pdf
http://psasir.upm.edu.my/id/eprint/81145/
https://www.sciencedirect.com/science/article/pii/S2405844018377934#!
_version_ 1677783009924743168
score 13.211869

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