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Explainable machine learning techniques for hybrid nanofluids transport characteristics: an evaluation of shapley additive and local interpretable model-agnostic explanations

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dc.citedby1
dc.contributor.authorKanti P.K.en_US
dc.contributor.authorPrabhakarSharmaen_US
dc.contributor.authorWanatasanappan V.V.en_US
dc.contributor.authorSaid N.M.en_US
dc.contributor.authorid57216493630en_US
dc.contributor.authorid59380697400en_US
dc.contributor.authorid57217224948en_US
dc.contributor.authorid57217198447en_US
dc.date.accessioned2025-03-03T07:41:39Z
dc.date.available2025-03-03T07:41:39Z
dc.date.issued2024
dc.description.abstractComprehending and managing the transport characteristics of nanofluids is critical for improving their efficacy in heat transfer applications, thereby improving thermal management systems. This research focuses on investigating the impact of varying concentrations (0.05?1 vol.%) and temperatures (30?60��C) on the thermal conductivity and viscosity of water-based nanofluids. These nanofluids contain graphene oxide, silicon dioxide, and titanium dioxide, as well as hybrid combinations thereof. The research revealed that nanofluids exhibit higher viscosity and thermal conductivity compared to water. The maximum thermal conductivity and viscosity of 1.52 and 2.77 are observed for GO for 1 vol% compared to the water at 60 and 30��C, respectively. Notably, graphene oxide nanofluid exhibits the highest thermal conductivity and viscosity among all the studied nanofluids. These findings imply that graphene oxide and its hybrid nanofluids hold promise for enhancing heat transfer and energy efficiency in various industrial applications. The modeling and simulation of hybrid nanofluids' thermophysical properties are difficult and time-consuming. Modern machine learning algorithms are capable of handling such complex data. As a result, in the current investigation, two distinct ensembles and deep learning-based techniques, deep neural networks and extreme�gradient boost, were used. The statistical examination of the viscosity model shows that the extreme�gradient boost-based model had an R2 value of 0.9122, while the deep neural network-based model had just 0.7371. The mean square error for the extreme�gradient boost-based model was just 0.010, whereas it climbed to 0.0329 for the deep neural network-based model. ? Akad�miai Kiad�, Budapest, Hungary 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s10973-024-13639-x
dc.identifier.epage11618
dc.identifier.issue21
dc.identifier.scopus2-s2.0-85207269187
dc.identifier.spage11599
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85207269187&doi=10.1007%2fs10973-024-13639-x&partnerID=40&md5=1dd13c373f64d51d470ea2410b1892a9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36234
dc.identifier.volume149
dc.pagecount19
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Thermal Analysis and Calorimetry
dc.subjectDeep neural networks
dc.subjectNanofluidics
dc.subjectThermal conductivity of liquids
dc.subjectExtreme gradient boost
dc.subjectGraphene oxides
dc.subjectHybrid nanofluid
dc.subjectNanofluids
dc.subjectNeural-networks
dc.subjectShapley
dc.subjectShapley additive explanation
dc.subjectThermal
dc.subjectTransport characteristics
dc.subjectTransport phenomenon
dc.subjectTitanium dioxide
dc.titleExplainable machine learning techniques for hybrid nanofluids transport characteristics: an evaluation of shapley additive and local interpretable model-agnostic explanationsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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