Reliable prediction of thermophysical properties of nanofluids for enhanced heat transfer in process industry: a perspective on bridging the gap between experiments, CFD and machine learning

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dc.authoridKhalid, Rehan Zubair/0009-0004-3183-673X
dc.authoridUllah, Atta/0000-0001-8010-3904
dc.contributor.authorUllah, Atta
dc.contributor.authorKılıç, Mustafa
dc.contributor.authorHabib, Ghulam
dc.contributor.authorSahin, Mahir
dc.contributor.authorKhalid, Rehan Zubair
dc.contributor.authorSanaullah, Khairuddin
dc.date.accessioned2025-01-06T17:43:46Z
dc.date.available2025-01-06T17:43:46Z
dc.date.issued2023
dc.description.abstractIn recent years, traditional fluids are frequently being replaced by efficient heat transfer fluids showing physical and thermal stability. One such category of fluids is called nanofluids, in which solid nanoparticles (metals or their oxides, nitrides and so on) are suspended in a base fluid resulting in enhanced heat transfer characteristics. These nanofluids are increasingly used in low to medium temperature applications toward intensification of process and power plants by reducing the overall size and heat losses. However, as compared to a pure fluid, prediction of thermal and physical properties of nanofluids is a challenge due to unavailability of a general model. These thermal and hydraulic characteristics are strongly dependent upon multiple factor including particle size, particle volume concentration, particle composition, particle shape, temperature, base fluid material, pH and shear rate. Keeping these challenges in mind and availability of modeling tools, we first summarize and comment on popular correlations available to predict thermal and physical properties of nanofluids. Then, a general approach for carrying out reliable computational fluid dynamics (CFD) simulations is presented. The limitation of a general correlation of physical properties for input into CFD code can be overcome by use of machine learning (ML) tools such as artificial neural networks (ANN) taking advantage of the huge databank of physical properties of nanofluids. The use of ML to compliment CFD for accurate and reliable simulation of systems employing nanofluids as working fluids is highlighted at the end as potential emerging areas of research. [GRAPHICS] .
dc.description.sponsorshipPakistan Institute of Engineering and Applied Sciences (PIEAS); Adana Alparslan Turkes Science and Technology University (ATU); Higher Education Commission (HEC) of Pakistan; [520-141007-2EG6-07]
dc.description.sponsorshipThe first, third and fifth authors acknowledge the support they received from Pakistan Institute of Engineering and Applied Sciences (PIEAS). The second and the fourth authors acknowledge the support they received from Adana Alparslan Turkes Science and Technology University (ATU). Mr. Rehan Zubair Khalid acknowledges the fellowship (520-141007-2EG6-07) he received from Higher Education Commission (HEC) of Pakistan for his PhD studies at PIEAS.
dc.identifier.doi10.1007/s10973-023-12083-7
dc.identifier.endpage5881
dc.identifier.issn1388-6150
dc.identifier.issn1588-2926
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85151981369
dc.identifier.scopusqualityQ1
dc.identifier.startpage5859
dc.identifier.urihttps://doi.org/10.1007/s10973-023-12083-7
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2780
dc.identifier.volume148
dc.identifier.wosWOS:000983785500002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Thermal Analysis and Calorimetry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectNanofluids
dc.subjectThermophysical properties
dc.subjectSimulation
dc.subjectArtificial neural networks
dc.titleReliable prediction of thermophysical properties of nanofluids for enhanced heat transfer in process industry: a perspective on bridging the gap between experiments, CFD and machine learning
dc.typeArticle

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