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Record W2319333178 · doi:10.1115/gt2007-27429

The Effect of Blade Leading Edge Platform Shape on Upstream Disk Cavity to Mainstream Flow Interaction of a High-Pressure Turbine Stage

2007· article· en· W2319333178 on OpenAlexfundno aff
Remo Marini, Sami Girgis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
FundersPratt and Whitney Canada
KeywordsLeading edgeUpstream (networking)Flow (mathematics)TransonicMechanicsTurbineEnhanced Data Rates for GSM EvolutionComputational fluid dynamicsMaterials scienceStage (stratigraphy)VortexBlade (archaeology)Turbine bladeMechanical engineeringEngineeringGeologyPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a CFD study of a transonic highpressure 1-stage turbine that includes the blade upstream disk cavity. The emphasis of the analysis was to understand and quantify the impact of the blade leading edge platform shape on the flow interaction between the upstream disk cavity flow and the gaspath mainstream flow. Two blade platform shapes were analyzed: a recessed and a raised leading edge shape. The results presented include steadystate and transient simulations in order to describe the flow interaction and quantify the impact on stage efficiency. A sensitivity analysis on the amount of cavity flow was performed to investigate the impact on secondary losses (interpreted by entropy generation) and stage efficiency. It was found that the blade leading edge platform shape and cavity flow amount affected the blade hub passage vortex structure and location. At the nominal engine condition, the raised leading edge platform shape showed an improvement in stage efficiency. It also showed a reduced sensitivity of stage efficiency due to cavity flow amount.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.006
GPT teacher head0.234
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

Citations20
Published2007
Admission routes1
Has abstractyes

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