MétaCan
Menu
Back to cohort
Record W4256220087 · doi:10.32920/ryerson.14665287

Experimental Study Of Short Hole Film-Cooling

2021· preprint· en· W4256220087 on OpenAlexaff
Mohammed A. Gandhi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLift (data mining)Wind tunnelRowUpstream (networking)TurbulenceMechanicsJet (fluid)Wind speedMaterials scienceOpticsPhysicsMeteorologyEngineeringComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

An experimental study was conducted to investigate the film cooling effectiveness of a few configurations of short injection holes: single row, double row and both of the preceding cases with an upstream ramp placed at two different locations. In order to perform the above study, a wind-tunnel facility was assembled to facilitate in the successful culmination of the experiments. The focus of the study was to determine the cooling provided by the short injection holes at a variety of blowing ratios and whether adding an extra row of holes, upstream of the first row would make a difference. For the second part, a ramp was placed upstream of the single and double row configuration to help improve cooling . All of the experiments were performed in a low speed wind-tunnel with a mainstream velocity of 8 m/s and a turbulence insity of 3.3%. Higher blowing ratios were ineffective in improving film-cooling effectiveness due to jet lift-off. Two rows of holes increased the cooling effectiveness by 200%, when compared to single row configurations at the same blowing ratio without ramps. Upstream ramps provided significant improvement in the near hole region of the injection holes.

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 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 score0.077
Threshold uncertainty score0.672

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)0.0000.000

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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicTurbomachinery Performance and OptimizationFrench-language works237,207