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Record W2745638982 · doi:10.1299/jsmeimpt.2017.04-06

ANALYSIS OF THE DYNAMIC BEHAVIOUR OF MULTI-MESH SPUR AND HELICAL GEARS - APPLICATION TO THE DEFINITION OF OPTIMUM PROFILE RELIEFS IN AERONAUTICAL TRANSMISSIONS

2017· article· en· W2745638982 on OpenAlexaff
Hassen FACKFACK, Philippe Velex, Jérôme Bruyère, S. Becquerelle

Bibliographic record

VenueThe Proceedings of the JSME international conference on motion and power transmissions · 2017
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsPinionVibrationInvoluteTransmission (telecommunications)Computer scienceStructural engineeringPower (physics)EngineeringAcousticsMechanical engineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper is focused on the modelling and analysis of vibrations and dynamic loads in aeronautical multi-mesh gears comprising several spatial gear arrangements (idler gears, several pinions on one shaft. Optimum profile modifications are sought by applying a metaheuristic Genetic Algorithm to the local quasi-static transmission errors under load associated with each individual mesh. It is shown that the resulting linear symmetric profile modifications all lie in the vicinity of the so-called analytical Master Curves initially defined for a single pinion-gear pair. The theory is applied to a 6-mesh aeronautical transmission and it is found that the proposed profile modifications can effectively reduce dynamic overloads and improve the vibrational behaviour of complex multi-mesh gears with different power circulations. Finally, it is shown that for systems submitted to several load levels, short optimal reliefs seem preferable with regard to vibration levels.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.216

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 science0.0010.000
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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

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