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Record W2054342653 · doi:10.1115/gt2007-28036

Simulation of a Multi-Stage Cooling Scheme for Gas Turbine Engines: Part I

2007· article· en· W2054342653 on OpenAlexaff
Mohamed Ghorab, Ibrahim Hassan, M. Beauchamp

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsCoolantTurbine bladeMechanical engineeringHeat transferAir coolingOffset (computer science)Materials scienceMechanicsGas turbinesTurbineAerospace engineeringEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

This paper presents heat transfer characteristics for a Multi-Stage Cooling Scheme (MSCS) design applicable to high temperature gas turbine engines in aerospace and electric power generation. The film cooling and impingement techniques are considered concurrently throughout this study. The proposed design involves passing cooling air from the inside of the turbine blade to the outside through three designed stages. The coolant air is passed through a circular hole into an internal gap creating an impingement of air inside the blade. It then exits through a sequence of two differently shaped holes onto the blade’s external surface. The film cooling effectiveness is enhanced by increasing the internal gap height and offset distance. This effect is significantly diminished however by changing the inclination angle from 90° to 30° at large gap height. The coolant momentum became more uniform by creating the internal gap consequently the coolant air is spread closer to the external blade surface. This reduces jet liftoff as the air exits its hole and also provides internal cooling for the blade. The hole exit positioned on the outer surface of the blade is designed to give a positive and a wide downstream lateral spreading. The MSCS demonstrates greater film cooling effectiveness performance than traditional schemes.

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 score0.711
Threshold uncertainty score0.323

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 head0.029
GPT teacher head0.278
Teacher spread0.250 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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