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Record W2025346599 · doi:10.1115/1.1645534

Influence of Loading Distribution on the Performance of Transonic High Pressure Turbine Blades

2004· article· en· W2025346599 on OpenAlexaff
D. Corriveau, S. A. Sjolander

Bibliographic record

VenueJournal of Turbomachinery · 2004
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsAirfoilMach numberTransonicTurbineStructural engineeringTurbine bladeReynolds numberDrag divergence Mach numberEngineeringAerospace engineeringMaterials scienceMechanicsAerodynamicsPhysicsTurbulence

Abstract

fetched live from OpenAlex

Midspan measurements were made in a transonic wind tunnel for three high pressure turbine blade cascades at design incidence. The baseline profile is the midspan section of a high pressure turbine blade of fairly recent design. It is considered mid-loaded. To gain a better understanding of blade loading limits and the influence of loading distributions, the profile of the baseline airfoil was modified to create two new airfoils having aft-loaded and front-loaded pressure distributions. Tests were performed for exit Mach numbers between 0.6 and 1.2. In addition, measurements were made for an extended range of Reynolds numbers for constant Mach numbers of 0.6, 0.85, 0.95, and 1.05. At the design exit Mach number of 1.05, the aft-loaded airfoil showed a reduction of almost 20% in the total pressure losses compared with the baseline airfoil. However, it was also found that for Mach numbers higher than the design value the performance of the aft-loaded blade deteriorated rapidly. The front-loaded airfoil showed generally inferior performance compared with the baseline airfoil.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.004
GPT teacher head0.185
Teacher spread0.182 · 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

Citations31
Published2004
Admission routes1
Has abstractyes

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