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Record W4296654887 · doi:10.1115/1.4055681

Methodology for the Redesign of Compressor Blades Undergoing Nonlinear Structural Interactions: Application to Blade-Tip/Casing Contacts

2022· article· en· W4296654887 on OpenAlexafffund
Solène Kojtych, Florence Nyssen, Charles Audet, Alain Batailly

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2022
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsGroup for Research in Decision AnalysisPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBlade (archaeology)CasingRotor (electric)Context (archaeology)Gas compressorNonlinear systemMechanical engineeringProcess (computing)Structural engineeringEngineeringComputer scienceGeology

Abstract

fetched live from OpenAlex

Abstract Over the past decade, the drive towards more efficient aircraft engines has pushed the boundaries of operating ranges far beyond a linear structural context. Nonlinear interfaces, such as blade-tip/casing contacts, are to be expected in nominal operating conditions. However, current blade design methodologies still rely on empirical structural considerations, often linear, which may lead to costly redesign operations. This work aims at proposing a methodology for the redesign of blades undergoing nonlinear structural interactions. A three-step redesign process is considered: (1) parameterization of an existing blade, (2) update of blade parameters with respect to a surrogate performance criterion, and (3) performance check of the optimized blade. An original two-way parameterization method is proposed to parameterize existing blades and generate models from blade parameters. As a proof-of-concept, the redesign of the NASA compressor blade rotor 37 and fan blade rotor 67 with respect to blade-tip/casing contacts is considered. High-fidelity parameterized models of the initial blades are obtained and their dynamic response to contact interactions are analyzed. Geometries are updated with respect to their clearance consumption, as its minimization has shown beneficial effects on the considered contact interactions. The proposed methodology allows us to better assess the relevance of this performance criterion in the context of blade-tip/casing 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 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: none
Teacher disagreement score0.812
Threshold uncertainty score0.431

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 head0.025
GPT teacher head0.281
Teacher spread0.256 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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