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Record W4386167475 · doi:10.2514/1.j062619

Two-Way Blade Modeling Method for Structural Redesign of Compressor Blades

2023· article· en· W4386167475 on OpenAlexafffund
Solène Kojtych, Charles Audet, Alain Batailly

Bibliographic record

VenueAIAA Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBlade (archaeology)TurbomachineryGas compressorRotor (electric)Nonlinear systemEngineeringRobustness (evolution)Mechanical engineeringCasingProcess (computing)Computer scienceControl engineering

Abstract

fetched live from OpenAlex

Over the past few years, stringent environmental requirements and the need for increased overall efficiency have forced designers to bring turbomachine components closer to their operating limits. To address lifespan issues, costly redesign operations are thus unavoidable. These operations face many roadblocks, especially when they are triggered by nonlinear phenomena for which there exists no design guidelines. For aircraft engine blades, the handling of nonlinear structural interactions is a major challenge. This works proposes a proof of concept for the redesign of compressor blades undergoing structural contact interactions at the blade-tip/casing interface. The redesign process involves the modeling of an existing input blade, followed by a shape update based on an iterative optimization algorithm. A two-way modeling method is proposed to parameterize the input blade and generate a computer-aided design model from blade parameters describing several conical blade sections. The fidelity of the parameterized blade with respect to the input blade is assessed for the NASA blades rotor 37 and rotor 67. A high fidelity is observed with respect to geometric and dynamic characteristics. The modeling method is fully compatible with an iterative redesign process: it is applied to the redesign of rotor 37 to increase its robustness to contact interactions.

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 score0.637
Threshold uncertainty score0.366

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 head0.028
GPT teacher head0.295
Teacher spread0.266 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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