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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 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 score0.293
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
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 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
Published2021
Admission routes1
Has abstractyes

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