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Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio

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dc.citedby50
dc.contributor.authorVicki Wanatasanappan V.en_US
dc.contributor.authorKumar Kanti P.en_US
dc.contributor.authorSharma P.en_US
dc.contributor.authorHusna N.en_US
dc.contributor.authorAbdullah M.Z.en_US
dc.contributor.authorid58093867000en_US
dc.contributor.authorid57216493630en_US
dc.contributor.authorid58961316700en_US
dc.contributor.authorid58093980000en_US
dc.contributor.authorid31567537400en_US
dc.date.accessioned2024-10-14T03:18:40Z
dc.date.available2024-10-14T03:18:40Z
dc.date.issued2023
dc.description.abstractThe present study is a pure experimental investigation of the viscosity and rheological properties of the Al2O3-Fe2O3 hybrid nanofluid and the development of a new correlation. The main purpose of the study is to evaluate the effect of the Al2O3-Fe2O3 mixture ratio on the viscosity property and develop a correlation for the viscosity prediction. The Al2O3 and Fe2O3 were first characterized using XRD diffraction and the FESEM technique. The nanofluid was prepared using a two-step method using base fluid consisting of water and ethylene glycol mixture at 60/40 ratios. Five different Al2O3-Fe2O3 nanoparticle compositions were investigated experimentally for the viscosity and rheological properties at temperatures between 0 and 100 �C. The experimental data shows that the Al2O3-Fe2O3 composition of 40/60 resulted in the highest viscosity value at all temperatures investigated, while the 60/40 composition recorded the lowest viscosity value. Besides, the increase in temperature of nanofluid shows a maximum viscosity reduction of 87.2 % as the temperature is increased from 0 to 100 �C. Also, the rheological analysis on a hybrid nanofluid for all compositions of Al2O3-Fe2O3 indicates a Newtonian fluid characteristic. The experimental research data was utilized to create an artificial neural network (ANN)-based architecture. An autoregressive method called the Bayesian approach was adopted for training hyperparameters. During model training, the autoregressive technique assisted in achieving outstanding correlation values of more than 99.99 % with minimal mean squared errors as low as 0.000036. � 2023 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo121365
dc.identifier.doi10.1016/j.molliq.2023.121365
dc.identifier.scopus2-s2.0-85147547914
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85147547914&doi=10.1016%2fj.molliq.2023.121365&partnerID=40&md5=9f923f5b261293787bbeb0e28cf2e61b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34256
dc.identifier.volume375
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Molecular Liquids
dc.subjectAl<sub>2</sub>O<sub>3</sub>-Fe<sub>2</sub>O<sub>3</sub>
dc.subjectANN
dc.subjectBayesian optimization
dc.subjectHybrid nanofluid
dc.subjectMachine learning
dc.subjectRheological behaviour
dc.subjectViscosity
dc.subjectAlumina
dc.subjectAluminum oxide
dc.subjectBayesian networks
dc.subjectEthylene
dc.subjectEthylene glycol
dc.subjectHematite
dc.subjectMachine learning
dc.subjectNanofluidics
dc.subjectNeural networks
dc.subjectNewtonian liquids
dc.subjectRheology
dc.subjectAl2O3-fe2O3
dc.subjectBayesian optimization
dc.subjectHybrid nanofluid
dc.subjectMachine-learning
dc.subjectMixture ratio
dc.subjectNanofluids
dc.subjectNew correlations
dc.subjectRheological behaviour
dc.subjectRheological property
dc.subjectViscosity properties
dc.subjectViscosity
dc.titleViscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratioen_US
dc.typeArticleen_US
dspace.entity.typePublication
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