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Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach

Ceramic nanoparticles have become increasingly popular owing to their wide range of engineering applications in the industry. Silica is one of the most promising nanomaterials for heat transfer applications due to its favorable thermophysical characteristics and dispersion stability. This work inves...

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Main Authors: Sharma, K.V., Talpa Sai, P.H.V.S., Sharma, P., Kanti, P.K., Bhramara, P., Akilu, S.
Format: Article
Published: Elsevier Ltd 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37301/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167809881&doi=10.1016%2fj.engappai.2023.106967&partnerID=40&md5=e658726409ceadf1be85bccce4b74934
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spelling oai:scholars.utp.edu.my:373012023-10-04T08:37:30Z http://scholars.utp.edu.my/id/eprint/37301/ Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach Sharma, K.V. Talpa Sai, P.H.V.S. Sharma, P. Kanti, P.K. Bhramara, P. Akilu, S. Ceramic nanoparticles have become increasingly popular owing to their wide range of engineering applications in the industry. Silica is one of the most promising nanomaterials for heat transfer applications due to its favorable thermophysical characteristics and dispersion stability. This work investigates the thermophysical properties of polydisperse SiO2 nanoparticles in a glycerol and water mixture. Aqueous glycerol nanofluid comprised 30 glycerol (30 GW) by weight, and SiO2 with mean particle sizes of 15, 50, and 100 nm were prepared for 0.5 and 1.0 volume concentrations using a two-step method. Measurements of properties are made in the temperature range of 30�100oC. The nanofluids behaved as a single-phase liquid remaining stable for four weeks. The viscosity and density of the base liquid and nanofluid decreased while the thermal conductivity increased with temperature. The base liquid-specific heat increased slightly while that of nanofluid decreased significantly with temperature. For 0.5 SiO2 concentration, the thermal conductivity and viscosity varied by 11.1 and 32, respectively at 60oC compared to the base liquid. Correlations are developed for the estimation of nanofluid properties. An Explainable Artificial Intelligence (XAI) technique called Bayesian approach optimized Gaussian Process Regression was employed to develop a prognostic model for the thermophysical properties of test nanofluids. In addition, the model's predictability and explainability are both enhanced by the use of kernel functions, which makes it possible to include historical data in the representation of a phenomenon. The test XAI approaches were shown as robust one because of the high correlation values, which ranged from 99.68 to 99.99, along with minimal modeling errors. © 2023 Elsevier Ltd Elsevier Ltd 2023 Article NonPeerReviewed Sharma, K.V. and Talpa Sai, P.H.V.S. and Sharma, P. and Kanti, P.K. and Bhramara, P. and Akilu, S. (2023) Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach. Engineering Applications of Artificial Intelligence, 126. ISSN 09521976 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167809881&doi=10.1016%2fj.engappai.2023.106967&partnerID=40&md5=e658726409ceadf1be85bccce4b74934 10.1016/j.engappai.2023.106967 10.1016/j.engappai.2023.106967 10.1016/j.engappai.2023.106967
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Ceramic nanoparticles have become increasingly popular owing to their wide range of engineering applications in the industry. Silica is one of the most promising nanomaterials for heat transfer applications due to its favorable thermophysical characteristics and dispersion stability. This work investigates the thermophysical properties of polydisperse SiO2 nanoparticles in a glycerol and water mixture. Aqueous glycerol nanofluid comprised 30 glycerol (30 GW) by weight, and SiO2 with mean particle sizes of 15, 50, and 100 nm were prepared for 0.5 and 1.0 volume concentrations using a two-step method. Measurements of properties are made in the temperature range of 30�100oC. The nanofluids behaved as a single-phase liquid remaining stable for four weeks. The viscosity and density of the base liquid and nanofluid decreased while the thermal conductivity increased with temperature. The base liquid-specific heat increased slightly while that of nanofluid decreased significantly with temperature. For 0.5 SiO2 concentration, the thermal conductivity and viscosity varied by 11.1 and 32, respectively at 60oC compared to the base liquid. Correlations are developed for the estimation of nanofluid properties. An Explainable Artificial Intelligence (XAI) technique called Bayesian approach optimized Gaussian Process Regression was employed to develop a prognostic model for the thermophysical properties of test nanofluids. In addition, the model's predictability and explainability are both enhanced by the use of kernel functions, which makes it possible to include historical data in the representation of a phenomenon. The test XAI approaches were shown as robust one because of the high correlation values, which ranged from 99.68 to 99.99, along with minimal modeling errors. © 2023 Elsevier Ltd
format Article
author Sharma, K.V.
Talpa Sai, P.H.V.S.
Sharma, P.
Kanti, P.K.
Bhramara, P.
Akilu, S.
spellingShingle Sharma, K.V.
Talpa Sai, P.H.V.S.
Sharma, P.
Kanti, P.K.
Bhramara, P.
Akilu, S.
Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach
author_facet Sharma, K.V.
Talpa Sai, P.H.V.S.
Sharma, P.
Kanti, P.K.
Bhramara, P.
Akilu, S.
author_sort Sharma, K.V.
title Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach
title_short Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach
title_full Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach
title_fullStr Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach
title_full_unstemmed Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (XAI) approach
title_sort prognostic modeling of polydisperse sio2/aqueous glycerol nanofluids' thermophysical profile using an explainable artificial intelligence (xai) approach
publisher Elsevier Ltd
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37301/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167809881&doi=10.1016%2fj.engappai.2023.106967&partnerID=40&md5=e658726409ceadf1be85bccce4b74934
_version_ 1779441362283790336
score 13.211869

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