Publication:
Experimental and explainable machine learning approach on thermal conductivity and viscosity of water based graphene oxide based mono and hybrid nanofluids

Simple item page
dc.citedby1
dc.contributor.authorKanti P.K.en_US
dc.contributor.authorParamasivam P.en_US
dc.contributor.authorWanatasanappan V.V.en_US
dc.contributor.authorDhanasekaran S.en_US
dc.contributor.authorSharma P.en_US
dc.contributor.authorid57216493630en_US
dc.contributor.authorid57283686300en_US
dc.contributor.authorid57217224948en_US
dc.contributor.authorid57205679715en_US
dc.contributor.authorid58961316700en_US
dc.date.accessioned2025-03-03T07:41:24Z
dc.date.available2025-03-03T07:41:24Z
dc.date.issued2024
dc.description.abstractThis study explores the thermal conductivity and viscosity of water-based nanofluids containing silicon dioxide, graphene oxide, titanium dioxide, and their hybrids across various concentrations (0 to 1 vol%) and temperatures (30 to 60��C). The nanofluids, characterized using multiple methods, exhibited increased viscosity and thermal conductivity compared to water, with hybrid nanofluids showing superior performance. Graphene oxide nanofluids displayed the highest thermal conductivity and viscosity ratios, with increases of 52% and 177% at 60��C and 30��C, respectively, for a concentration of 1 vol% compared to base fluid. Similarly, graphene oxide-TiO2 hybrid nanofluids achieved thermal conductivity and viscosity ratios exceeding 43% and 144% compared to the base fluid at similar conditions. This data highlights the significance of nanofluid concentration in influencing thermal conductivity, while temperature was found to have a more pronounced effect on viscosity. To tackle the challenge of modeling the thermophysical properties of these hybrid nanofluids, advanced machine learning models were applied. The Random Forest (RF) model outperformed others (Gradient Boosting and Decision Tree) in both the cases of thermal conductivity and viscosity with greater adaptability to handle fresh data during model testing. Further analysis using shapely additive explanations based on cooperative game theory revealed that relative to temperature, nanofluid concentration contributes more to the predictions of the thermal conductivity ratio model. However, the effect of nanofluid concentration was more dominant in the case of viscosity ratio model. ? The Author(s) 2024.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo30967
dc.identifier.doi10.1038/s41598-024-81955-1
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85213534767
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85213534767&doi=10.1038%2fs41598-024-81955-1&partnerID=40&md5=57bc1dad358a5d11c3deea34c235e194
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36115
dc.identifier.volume14
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.subjectgraphene oxide
dc.subjectnanofluid
dc.subjectsilicon dioxide
dc.subjectarticle
dc.subjectcontrolled study
dc.subjectdecision tree
dc.subjectexplainable machine learning
dc.subjectgame
dc.subjecthybrid
dc.subjectmachine learning
dc.subjectpharmaceutics
dc.subjectprediction
dc.subjectrandom forest
dc.subjecttemperature
dc.subjectthermal conductivity
dc.subjectviscosity
dc.subjectwater
dc.titleExperimental and explainable machine learning approach on thermal conductivity and viscosity of water based graphene oxide based mono and hybrid nanofluidsen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS