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Record W2615631135 · doi:10.1002/htj.21287

Numerical investigation of the effects of geometric parameters of heaters on mixed covection in a lid‐driven cavity filled with different nanofluids

2017· article· en· W2615631135 on OpenAlexaff
Mahmoud Salari, Ali Mohammadtabar, Mohammad Mohammadtabar

Bibliographic record

VenueHeat Transfer-Asian Research · 2017
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNusselt numberNanofluidEnclosureStreamlines, streaklines, and pathlinesMechanicsRayleigh numberMaterials scienceReynolds numberNatural convectionThermodynamicsVolume (thermodynamics)Heat transferPhysicsElectrical engineeringEngineeringTurbulence

Abstract

fetched live from OpenAlex

Abstract In this paper, the effects of the thicknesses and locations of two rectangular heaters, located on the bottom and one side of on an enclosure, on mixed convection of nanofluid flows in a lid‐driven cavity are numerically investigated. The enclosure is simultaneously heated partially by these two heaters which have similar or different thicknesses and also filled with different nanofluids containing nanoparticles of Cu, Ag, Al 2 O 3 , and TiO 2 within the base fluid of water. A finite volume approach by the SIMPLE algorithm is used to solve the governing equations. The effects of different Rayleigh numbers ( ), Reynolds numbers ( ), solid volume fractions ( ), heater lengths ( ), heater locations ( ) and heater thicknesses ( ) on the streamlines, isotherms and the average Nusselt number along two heaters are studied accurately. Also, variations of average Nusselt number of two heaters are considered whenever one heater is fixed and the other heater moves along on the wall. Moreover, variations of the length of one heater on the average Nusselt number are also studied whenever the length of the other heater is fixed. In addition, variations of the thickness of one heater on the average Nusselt number are studied whenever the thickness of the other heater is fixed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.265
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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