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Record W2062094935 · doi:10.2514/1.35230

Detached-Eddy Simulation of a Louver-Cooling Scheme for Turbine Blades

2008· article· en· W2062094935 on OpenAlexaff
C.X‐Z. Zhang, Sung In Kim, Ibrahim Hassan

Bibliographic record

VenueJournal of Propulsion and Power · 2008
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsLouverLarge eddy simulationDetached eddy simulationMechanical engineeringMarine engineeringTurbine bladeAerospace engineeringTurbineGas turbinesComputational fluid dynamicsMechanicsNuclear engineeringEngineeringMeteorologyMaterials scienceEnvironmental scienceReynolds-averaged Navier–Stokes equationsTurbulencePhysics

Abstract

fetched live from OpenAlex

The performance of a louver-cooling scheme on a flat plate was analyzed using a detached-eddy-simulation turbulence model. It was assumed that the louver-cooling scheme was tested in a wind tunnel with the mainstream flow velocity of 20 m/s, equivalent to a Reynolds number of 16,200, based on the jet diameter. Turbulence closure was achieved by a realizable k-e-based detached-eddy-simulation turbulence model. Solutions of two blowing ratios of 0.5 and 1 were successfully obtained by running parallel on 16 nodes on a computer cluster. The flowfields were found to be highly unsteady and oscillatory in nature, with the maximum fluctuation of the adiabatic effectiveness as high as 15% of the time-averaged value. It is shown that the fluctuations in the adiabatic effectiveness are mainly caused by the spanwise fluctuation of the coolant jet and the unsteady vortical structures created by the interaction of the jet and the mainstream.

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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.243
Teacher spread0.228 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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