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Record W1978233777 · doi:10.1115/icef2013-19050

Simulation of Jet Impingement Heat Transfer

2013· article· en· W1978233777 on OpenAlexaff
G. Nasif, R. M. Barron, Ram Balachandar, O. Iqbal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNusselt numberMechanicsVolume of fluid methodJet (fluid)Heat transferReynolds-averaged Navier–Stokes equationsFinite volume methodNozzleReynolds numberHeat transfer coefficientHeat fluxMaterials scienceThermodynamicsPhysicsFlow (mathematics)Computational fluid dynamicsTurbulence

Abstract

fetched live from OpenAlex

A numerical investigation to determine flow and thermal characteristics of an unsubmerged axisymmetric oil jet impinging on a confined flat surface with uniform heat flux has been undertaken. Large impingement length to nozzle diameter ratios were chosen in the simulations. The volume of fluid (VOF) method utilizing a High Resolution Interface Capturing scheme (HRIC) was used to perform the two-phase (air-oil) simulations. The governing 3D Navier-Stokes equations and energy equation were numerically solved using a finite volume discretization on an unstructured mesh. A new methodology was developed to define the radial extent of the stagnation region and understand the variation of the heat transfer coefficient in this region. The normalized local Nusselt number profile was found to be slightly dependent on Reynolds number for a given nozzle size. Correlations to predict the dimensionless velocity gradient and the Nusselt number in the stagnation region were established.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.216
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2013
Admission routes1
Has abstractyes

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