Thermoelectric generation from vented cavities with a rotating conic object and highly conductive CNT nanofluids for renewable energy systems

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dc.contributor.authorSelimefendigil F.
dc.contributor.authorÖztop H.F.
dc.date.accessioned2024-07-22T08:06:12Z
dc.date.available2024-07-22T08:06:12Z
dc.date.issued2021
dc.description.abstractIn the present work, thermoelectric power generation from cavities with ventilation ports is considered by using a rotating conic object and carbon-nanotube particles in the base fluid. Effects of different pertinent parameters such as Reynolds numbers of hot and cold fluid streams (between 200 and 1000), rotational Reynolds number of the conic object (between −400 and 400), size (between 0.05H and 0.25H) and horizontal location (between 0.2H and 0.6H) of the object and nanoparticle volume fractions of nanoparticles (between 0 and 0.02) on fluid flow, interface temperature and generated thermoelectric output power characteristics were studied. It was observed that exit port location of the hot cavity, fluid stream Reynolds number and rotational Reynolds number of the object have significant impacts on the fluid flow, interface temperatures and output power. When lowest and highest fluid stream Reynolds number is compared, 43.75% variations in the output power is obtained. The clockwise rotation of the conic object results in higher thermoelectric power generations as compared to a stationary cone while up to 49.20% increments of the power is attained at the highest rotational speed. The size of the rotating object is influential on the power generation while its horizontal location has slight effects. When nanofluid at the highest solid volume fraction configuration is compared with pure water case, 20% increment in the thermoelectric power is obtained. A predictive model for power estimations with adaptive network-based fuzzy inference system is proposed for input parameters of hot and cold fluid stream Reynolds number and rotational Reynolds number of the conic object which delivers accurate and fast prediction results. © 2021 Elsevier Ltd
dc.identifier.DOI-ID10.1016/j.icheatmasstransfer.2021.105139
dc.identifier.issn07351933
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/13426
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.subjectCarbon nanotubes
dc.subjectFlow of fluids
dc.subjectFuzzy inference
dc.subjectFuzzy neural networks
dc.subjectLocation
dc.subjectNanofluidics
dc.subjectNanoparticles
dc.subjectPredictive analytics
dc.subjectRenewable energy resources
dc.subjectReynolds number
dc.subjectThermoelectric power
dc.subjectThermoelectric power plants
dc.subjectVolume fraction
dc.subjectAdaptive network based fuzzy inference system
dc.subjectInterface temperatures
dc.subjectNanoparticle volume fractions
dc.subjectPredictive modeling
dc.subjectRenewable energy systems
dc.subjectRotational reynolds numbers
dc.subjectSolid volume fraction
dc.subjectThermoelectric generation
dc.subjectThermoelectric energy conversion
dc.titleThermoelectric generation from vented cavities with a rotating conic object and highly conductive CNT nanofluids for renewable energy systems
dc.typeArticle

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