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Record W2043030287 · doi:10.1115/1.4028263

Redesign of a High-Pressure Compressor Blade Accounting for Nonlinear Structural Interactions

2014· article· en· W2043030287 on OpenAlexaff
Alain Batailly, Mathias Legrand, Antoine Millecamps, Sébastien Cochon, François Garcin

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2014
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsMcGill University
Fundersnot available
KeywordsAerodynamicsBlade (archaeology)Centrifugal compressorGas compressorStructural engineeringCasingStiffeningRotor (electric)Nonlinear systemMechanical engineeringEngineeringComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

Recent numerical developments dedicated to the simulation of rotor/stator interaction involving direct structural contacts have been integrated within the Snecma industrial environment. This paper presents the first attempt to benefit from these developments and account for structural blade/casing contacts at the design stage of a high-pressure compressor blade. The blade of interest underwent structural divergence after blade/abradable coating contact occurrences on a rig test. The design improvements were carried out in several steps with significant modifications of the blade stacking law while maintaining aerodynamic performance of the original blade design. After a brief presentation of the proposed design strategy, basic concepts associated with the design variations are recalled. The iterated profiles are then numerically investigated and compared with respect to key structural criteria such as: (1) their mass, (2) the residual stresses stemming from centrifugal stiffening, (3) the vibratory level under aerodynamic forced response, and (4) the vibratory levels when unilateral contact occurs. Significant improvements of the final blade design are found: the need for an early integration of nonlinear structural interactions criteria in the design stage of modern aircraft engines components is highlighted.

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 categoriesnone
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 score0.449
Threshold uncertainty score0.521

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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.

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

Citations17
Published2014
Admission routes1
Has abstractyes

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