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Record W2067218291 · doi:10.2514/1.3856

Numerical Heat Transfer Correlation for Array of Hot-Air Jets Impinging on 3-Dimensional Concave Surface

2005· article· en· W2067218291 on OpenAlexafffund
Mathieu Fregeau, Farooq Saeed, Ion Paraschivoiu

Bibliographic record

VenueJournal of Aircraft · 2005
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaKing Fahd University of Petroleum and Minerals
KeywordsComputational fluid dynamicsNusselt numberMechanicsNozzleHeat transferJet (fluid)Mach numberInterpolation (computer graphics)Computer simulationMaterials sciencePhysicsClassical mechanicsThermodynamicsReynolds numberTurbulence

Abstract

fetched live from OpenAlex

Numerical heat transfer correlations established from a numerical computational fluid dynamics (CFD) study of a three-dimensional hot-air jet array impinging on curved (circular) surface are presented. The results are in the form of numerical correlations for the average and maximum Nusselt number for different nozzle-to-nozzle spacing, nozzle-to-surface height, and hot-air jet Mach numbers typical of those in an hot-air antiicing system employed on aircraft wings. A validation case is presented, and it is shown that the results obtained from the CFD study are in good agreement with experimental data found in the literature. An interpolation technique, the Dual-Kriging method, that makes use of the numerical database for antiicing simulation on aircraft wings is presented. The benefit of using the Dual-Kriging method is that it preserves the nonlinear nature of the heat transfer distribution from a hot-air jet impinging on a curved surface.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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

Citations49
Published2005
Admission routes2
Has abstractyes

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