MétaCan
Menu
Back to cohort
Record W3208448301 · doi:10.1139/tcsme-2021-0014

Effect of blowing ratio on film-cooling effectiveness of ginkgo shaped holes: a numerical approach

2021· article· en· W3208448301 on OpenAlexvenueno aff
Muhammad Awais, Reaz Hasan, M. Hamidur Rahman

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceMechanicsCoolantTurbine bladeTurbulenceMechanical engineeringHeat transferTurbinePhysicsEngineering

Abstract

fetched live from OpenAlex

Modern gas turbine engines operate at high temperatures to improve thermal efficiency and power output. Increased rotor inlet temperatures increase the rate of heat transfer to the turbine blades, which requires sophisticated cooling schemes to keep the blade temperature at acceptable levels. This work is a numerical investigation of film cooling techniques as applied to gas turbines. The cooling performance of two differently shaped holes, namely, Ginkgo Forward and Ginkgo Reverse, were investigated in terms of centerline and local lateral cooling effectiveness, and a comprehensive comparison was made with the cooling performance of a cylindrical hole. The investigations were performed at a constant density ratio (DR = 2.0) and three different blowing ratios (BR = 1.0, 1.5, and 2.0). Under all of the operating conditions, the results demonstrated significant augmentation in centerline and lateral cooling effectiveness when the Ginkgo Reverse shaped hole was used, followed by the Ginkgo Forward and cylindrical cooling holes. For the shaped cooling holes, the low velocity gradient through the film alleviated the jet lift-off and turbulence, resulting in decreased entrainment of hot gas to the bottom surface. To conclude, the prominent lateral dispersal of the coolant due to the shaped cooling holes significantly enhanced thermal protection and the overall cooling performance.

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: none
Teacher disagreement score0.854
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicTurbomachinery Performance and OptimizationFrench-language works237,207