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Effect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysis

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dc.citedby25
dc.contributor.authorKumar K P.en_US
dc.contributor.authorAlruqi M.en_US
dc.contributor.authorHanafi H.A.en_US
dc.contributor.authorSharma P.en_US
dc.contributor.authorWanatasanappan V.V.en_US
dc.contributor.authorid58803258700en_US
dc.contributor.authorid57225072010en_US
dc.contributor.authorid36772441100en_US
dc.contributor.authorid58961316700en_US
dc.contributor.authorid57217224948en_US
dc.date.accessioned2025-03-03T07:44:35Z
dc.date.available2025-03-03T07:44:35Z
dc.date.issued2024
dc.description.abstractIn this study, the current research delves into the influence of nanoparticle size on turbulent forced convective heat transfer, entropy generation, and friction factor. The investigation focused on three different sizes (30 nm, 50 nm, and 80 nm) of Al2O3 nanoparticles (NPs) suspended in a water-based nanofluid (NF) with a 1 vol% concentration flow in a circular tube. The nanoparticles (NPs) were characterized using various characterization techniques. The stability and pH of the NF were determined, and its viscosity (VST) and thermal conductivity (TC) were measured at a temperature of 60 �C. Heat transfer experiments were conducted with varying particle sizes and Reynolds number (Re), maintaining a fluid inlet temperature of 60 �C. The results indicated that the NF containing 30 nm particles exhibited higher VST and TC compared to the other samples and the base fluid. The maximum enhancement in Nu for Al2O3 (30 nm) and Al2O3 (80 nm) NFs is 60.7 and 18.5 % greater than that of base fluid, respectively. The maximum and minimum total entropy generation (Sgen,T) value of 0.499 and 0.286 observed for base fluid and Al2O3 NF (30 nm), respectively at low Re. The highest friction factor enhancement for Al2O3 NF (30 nm) exceeded by 9.4 % compared to the base fluid, and the maximum thermal performance factor observed for Al2O3 NF (30 nm) was 1.57. Finally, regression analysis was employed to establish correlations for estimating Nu and friction factor values. Prognostic models were developed using two sophisticated machine learning algorithms, XGBoost and Gradient Boosting Regression (GBR). Both models demonstrated exceptional prediction abilities, achieving over 99 % accuracy rates based on the experimental data. ? 2023 Elsevier Masson SASen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo108825
dc.identifier.doi10.1016/j.ijthermalsci.2023.108825
dc.identifier.scopus2-s2.0-85179841641
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85179841641&doi=10.1016%2fj.ijthermalsci.2023.108825&partnerID=40&md5=8883a2b212f58aa29b07e063b20a3766
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36776
dc.identifier.volume197
dc.publisherElsevier Masson s.r.l.en_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Thermal Sciences
dc.subjectAdaptive boosting
dc.subjectAlumina
dc.subjectAluminum oxide
dc.subjectEntropy
dc.subjectFactor analysis
dc.subjectFriction
dc.subjectHeat convection
dc.subjectMachine learning
dc.subjectNanofluidics
dc.subjectNanoparticles
dc.subjectParticle size
dc.subjectParticle size analysis
dc.subjectRegression analysis
dc.subjectReynolds number
dc.subjectSpecific heat
dc.subjectThermal conductivity of liquids
dc.subjectEntropy generation
dc.subjectEvacuated tubes
dc.subjectFriction factors
dc.subjectGradient boosting
dc.subjectNanofluids
dc.subjectOptimisations
dc.subjectParticles sizes
dc.subjectRenewable energies
dc.subjectSecond Law of Thermodynamics
dc.subjectThermo dynamic analysis
dc.subjectSolar energy
dc.titleEffect of particle size on second law of thermodynamics analysis of Al2O3 nanofluid: Application of XGBoost and gradient boosting regression for prognostic analysisen_US
dc.typeArticleen_US
dspace.entity.typePublication
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