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Record W2029355186 · doi:10.1002/cjce.22169

A numerical study of heat transfer in a turbulent pulsating impinging jet

2015· article· en· W2029355186 on OpenAlexaffvenue
Kazem Esmailpour, Mostafa Hosseinalipour, Behnam Bozorgmehr, Arun S. Mujumdar

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsMechanicsJet (fluid)Heat transferTurbulenceReynolds numberAmplitudeNozzleOscillation (cell signaling)PhysicsHeat transfer enhancementFlow (mathematics)ThermodynamicsOpticsChemistry

Abstract

fetched live from OpenAlex

Pulsating impinging jets can have significant influence on transfer processes. A number of studies have been done on heat transfer in a pulsating impinging jet but very divergent and sometimes contradictory results have been reported. In the present study, the flow and temperature field under a single confined pulsating turbulent impinging jet are determined numerically by the finite volume method. Effects of Pulsation function parameters (frequency and amplitude) and various geometries on the flow characteristics and heat transfer rate from hot surface are discussed. Results of simulation show that flow pulsation has different effects in various flow zones. Pulsation of jet improve cooling performance in the wall jet zone and simultaneously reduce heat transfer in the stagnation zone. As expected the effect of flow pulsation decays with distance from the jet nozzle. Under certain conditions flow oscillation adversely affects the heat transfer in comparison with the steady jet at the same mean Reynolds number. In pulsating jets we can introduce a critical frequency correspond to St = 0.26. The amount of heat transfer at the frequencies corresponding St = 0.26 is higher than other cases. In general, it is concluded that cooling performance of oscillating impinging jet is enhanced by increase in the frequency and amplitude of oscillation as well as decrease in nozzle to plate distance.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.017
GPT teacher head0.204
Teacher spread0.187 · 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 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

Citations23
Published2015
Admission routes2
Has abstractyes

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