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Record W1966336336 · doi:10.1115/gt2002-30004

An Engine Research Program Focused on Low Pressure Turbine Aerodynamic Performance

2002· article· en· W1966336336 on OpenAlexaboutno aff
Raymond Castner, Santo Chiappetta, John Wyzykowski, John J. Adamczyk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTurbofanTurbineComputational fluid dynamicsAerodynamicsAerospace engineeringPropulsionOperabilityComputer scienceReynolds numberTurbulenceMechanical engineeringSimulationMarine engineeringEngineeringAutomotive engineeringMeteorologyPhysicsReliability engineering

Abstract

fetched live from OpenAlex

A comprehensive test program was performed in the Propulsion Systems Laboratory at the NASA Glenn Research Center, Cleveland Ohio using a highly instrumented Pratt and Whitney Canada PW 545 turbofan engine. A key objective of this program was the development of a high-altitude database on small, high-bypass ratio engine performance and operability. In particular, the program documents the impact of altitude (Reynolds number) on the aero-performance of the low-pressure turbine (fan turbine). A second objective was to assess the ability of a state-of-the-art CFD code to predict the effect of Reynolds number on the efficiency of the low-pressure turbine. CFD simulation performed prior and after the engine tests will be presented and discussed. Key findings are the ability of a state-of-the art CFD code to accurately predict the impact of Reynolds number on the efficiency and flow capacity of the low-pressure turbine. In addition the CFD simulations showed the turbulence intensity exiting the low-pressure turbine to be high (9%). The level is consistent with measurements taken within an engine.

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)0.002
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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

Citations9
Published2002
Admission routes1
Has abstractyes

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