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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.440

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 head0.004
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