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Record W4236462987 · doi:10.32920/ryerson.14646639.v1

On the Fatigue and Fracture of Bladed and Integrally Bladed Rotors of Aircraft Engine Compressors

2021· preprint· en· W4236462987 on OpenAlexaff
Dikran Mangardich

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFrettingIntegrally closedStructural engineeringFracture mechanicsFracture (geology)Gas compressorMaterials scienceNucleationMechanicsEngineeringMechanical engineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

The fatigue and fracture for bladed and integrally bladed rotors (IBR) of aircraft engine compressors has been studied. For IBRs, a new distinct finite element technique was developed to model crack propagation under combined low cycle and high cycle fatigue loading. The crack trajectory, aspect ratio, and shape resulting from the method agreed very well with airfoils which fractured in service. The technique can be extended on other compressor disk applications. For bladed rotors limited by fretting fatigue, a unique fracture mechanics based methodology was developed for obtaining an evolved coefficient of friction (COF) resulting from fretting motion between the fan blade and hub. The predicted nucleation location, nucleation life, crack trajectory, shape and propagation life agreed well with the fractured components. The study confirms that the fretting-specific modified Smith-Watson-Topper (SWT) parameter more accurately predicts the nucleation location and life of the crack compared to the plain fatigue SWT parameter.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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