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Record W3208268992 · doi:10.37934/cfdl.13.10.2535

Conjugate Effect on the Thermal Characteristics of Air Impinging Jet

2021· article· en· W3208268992 on OpenAlexafffund
G. Nasif, Yasser El-Okda

Bibliographic record

VenueCFD Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of Windsor
FundersCompute Canada
KeywordsJet (fluid)Thermal conductivityMechanicsHeat transferMaterials scienceConvective heat transferStagnation pointConvectionHeat fluxTurbulenceThermodynamicsThermal conductionThermalPhysicsComposite material

Abstract

fetched live from OpenAlex

A computational fluid dynamics (CFD) investigation to determine the conjugate heat transfer (CHT) effect on the stagnation and local thermal characteristics due to an impinging process has been carried out in this study using STAR-CCM+ - Siemens PLM commercial code. The transient Navier-Stokes’s equations are numerically solved using a finite volume approach with k-ω SST eddy viscosity as the turbulence model. A fully developed circular air jet with different Reynolds numbers, impinging vertically onto a heated flat disc with different metals, thicknesses, and boundary heat fluxes are employed in the current study to examine the thermal characteristics and provide an enhanced picture for the convection mechanism that used in jet cooling technology. It is found that the thermal characteristics are influenced by the thermal conductivity and thickness of the target upon using air as a cooling jet. The CHT process enhances the local convective heat transfer at the fluid-solid interface due to the variation in transverse and axial conductive heat transfer inside the metal up to a certain redial extent from the stagnation region compared to the process with no CHT. The extent of the radial enhancement depends on the thermal conductivity of the metal. For a given thermal conductivity, the CHT process acts to increase the temperature and convective heat flux of the stagnation region as the metal thickness increases.

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 score0.006
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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