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Record W2059521908 · doi:10.1115/gt2012-68173

Numerical Study of a Rotor/Stator Interaction Case Experimentally Simulated With an Industrial Compressor

2012· preprint· en· W2059521908 on OpenAlexaff
Alain Batailly, Mathias Legrand, Antoine Millecamps, François Garcin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsMcGill University
FundersOffice National d'études et de Recherches AérospatialesSafranCentre National de la Recherche Scientifique
KeywordsCasingRotor (electric)Materials scienceGas compressorStatorBlade (archaeology)Distortion (music)Mechanical engineeringComputer simulationStructural engineeringComputer scienceEngineeringSimulation

Abstract

fetched live from OpenAlex

Higher aircraft energy efficiency may be achieved by minimizing the clearance between the rotating blade tips and respective surrounding casing. A common technical solution consists in the implementation of an abradable liner which improves both the operational safety and the efficiency of modern turbomachines. Recently, unexpected abradable wear removal mechanisms were observed in experimental set-ups and during maintenance procedures. The present study introduces a numerical strategy capable to address this occurrence. After focusing on the analysis of the experimental results, the good agreement between experimental observations and numerical results is illustrated in terms of critical stress levels within the blade as well as final wear profiles of the abradable liner. New blade designs are also explored in order to assess the impact of blade design on the outbreak of the interaction phenomenon. The prevalence of three dominant parameters in the interaction onset is shown: (1) blade design, (2) abradable material mechanical properties and (3) the need for a global distortion of the casing to synchronize blade-tip/abradable coating contacts.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.053
GPT teacher head0.300
Teacher spread0.248 · 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 teacher head, not a consensus.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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