MétaCan
Menu
Back to cohort
Record W2004125166 · doi:10.1115/ht2013-17695

Experimental Flow Field Investigations of Nozzle Film Cooling Scheme on a Flat Plate Using Stereo PIV

2013· article· en· W2004125166 on OpenAlexaff
Yingjie Zheng, Ibrahim Hassan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsConcordia University
Fundersnot available
KeywordsNozzleMechanicsJet (fluid)Wind tunnelReynolds numberVortexVorticityLift (data mining)Materials sciencePhysicsOpticsTurbulenceThermodynamicsComputer science

Abstract

fetched live from OpenAlex

This paper presents experimental flow field investigations of a film cooling scheme, referred to as nozzle scheme, on a flat plate using stereo PIV. The nozzle scheme has a cylindrical hole and internal obstacles to change the velocity distribution near the hole exit and hence the jet-mainstream interaction. Counter-rotating vortex pair (CRVP) is known to be one of the detrimental effects that affect the film cooling effectiveness. Previous CFD simulations demonstrated nozzle hole’s capability of reducing CRVP strength and enhancing film cooling effectiveness in comparison with a normal cylindrical hole. The present study examines the nozzle hole flow filed experimentally at blowing ratio ranged from 0.5 to 2.0 and compares with cylindrical hole. The experiments were conducted in a low-speed wind tunnel with a mainstream Reynolds number of 115,000 and the density ratio was 1.0 during all the investigations. The experimental results show that nozzle hole reduces streamwise vorticity of CRVP by an average of 55% at low blowing ratio, and 34%–40% at high blowing ratios. The velocity field and vorticity field of nozzle jet are compared with cylindrical jet. The result reveals that the nozzle jet forms a round bulk in contrast to the kidney shape jet core in cylindrical hole case. In addition, it is found that CRVP strength may not be a primary contributor to the jet lift-off.

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 categoriesInsufficient payload (model declined to judge)
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 score0.393
Threshold uncertainty score1.000

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)0.0010.000

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 head0.029
GPT teacher head0.240
Teacher spread0.210 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicHeat Transfer MechanismsFrench-language works237,207