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Record W1903840020 · doi:10.1115/1.4031865

Desensitization of Axial Compressor Performance and Stability to Tip Clearance Size

2015· article· en· W1903840020 on OpenAlexaff
Engin Erler, Huu Duc Vo, Hong Yu

Bibliographic record

VenueJournal of Turbomachinery · 2015
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsPolytechnique MontréalBombardier (Canada)
Fundersnot available
KeywordsTip clearanceAerodynamicsAxial compressorAirfoilMechanicsRotor (electric)Gas compressorSensitivity (control systems)PhysicsControl theory (sociology)Materials scienceVortexEngineeringComputer scienceThermodynamics

Abstract

fetched live from OpenAlex

This paper presents a computational and analytical study to identify and elucidate fundamental flow features associated with the desensitization of performance and aerodynamic stability of an axial compressor rotor to tip clearance change. Parametric studies of various design changes to a baseline double circular arc airfoil axial rotor led to the identification of two flow features associated with reducing sensitivity to tip clearance, namely, high incoming meridional momentum in the tip region and reduction/elimination of double tip leakage. Numerical experiments were subsequently performed on the baseline rotor geometry to validate these two flow features and explain the associated flow physics by variations in incoming meridional momentum and pitch size. Finally, two designs were proposed, namely, a full forward chordwise sweep (FFCS) rotor and a rotor with gradual stagger angle reduction in the outer span, to exploit these flow features. The results indicated that both designs produce the intended flow effects and exhibit lower sensitivity of performance and aerodynamic stability to tip clearance.

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)0.002
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
Metaresearch0.0000.002
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)0.0010.000

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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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

Citations29
Published2015
Admission routes1
Has abstractyes

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