MétaCan
Menu
Back to cohort
Record W3157367847 · doi:10.1108/hff-11-2020-0748

Free convective heat transfer efficiency in Al2O3–Cu/water hybrid nanofluid inside a rectotrapezoidal enclosure

2021· article· en· W3157367847 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Numerical Methods for Heat &amp Fluid Flow · 2021
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNanofluidMaterials scienceNusselt numberHeat transferThermodynamicsThermal conductivityHeat transfer enhancementRayleigh numberConvective heat transferMechanicsNatural convectionComposite materialHeat transfer coefficientReynolds numberTurbulencePhysics

Abstract

fetched live from OpenAlex

Purpose This paper aims to investigate numerically the free convective heat transfer efficiency inside a rectotrapezoidal enclosure filled with Al2O3–Cu/water hybrid fluid. The bottom wall of the cavity is uniformly heated, the upper horizontal wall is insulated, and the remaining walls are considered cold. A new thermophysical relation determining the thermal conductivity of the hybrid nanofluid has been established, which produced results those match with experimental ones. Design/methodology/approach The governing partial differential equations are solved using the finite element method of Galerkin type. The simulated results in terms of streamlines, heat lines and isotherms are displayed for various values of the model parameters, which govern the flow. Findings The Nusselt number, friction factor and the thermal efficiency index are also determined for the pertinent parameters varying different ratios of the hybrid nanoparticles. The simulated results showed that thermal buoyancy significantly controls the heat transfer, friction factor and thermal efficiency index. The highest thermal efficiency is obtained for the lowest Rayleigh number. Practical implications This theoretical study is significantly relevant to the applications of the hybrid nanofluids electronic devices cooled by fans, manufacturing process, renewable energies, nuclear reactors, electronic cooling, lubrication, refrigeration, combustion, medicine, thermal storage, etc. Originality/value The results showed that nanoparticle loading intensified the rate of heat transfer and thermal efficiency index at the expense of the higher friction factor or higher pumping power. The results further show that the heat transmission in Al2O3–Cu/water hybrid nanofluid at a fixed value of intensified $\phi_{hnf}$ compared to the Al2O3/water nanofluid when an amount of higher conductivity nanoparticles (Cu) added to it. Besides, the rate of heat transfer in Cu/water nanofluid declines when the lower thermal conductivity Al2O3 nanoparticles are added to the mixture.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.309
Teacher spread0.288 · 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