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Record W4366382679 · doi:10.4050/f-0077-2021-16886

The Effect of Hub Moment on Main Rotor Shaft Drive Gears

2021· article· en· W4366382679 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsTorqueDeflection (physics)EngineeringRotor (electric)Transverse planeHelicopter rotorStructural engineeringMoment (physics)Automotive engineeringAerospace engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

A common helicopter drive system configuration has a planetary final stage with the carrier driving the main rotor shaft. If the carrier is hard-mounted to the main rotor shaft, it is deflected by any main rotor hub moment or transverse load which bends the main rotor shaft. The deflection of the carrier is resisted at the sun and ring gear meshes. Thus, the planetary stage acts as a support that reacts to main rotor hub moments and transverse loads. The loads carried by the planetary stage of a S-92® helicopter in different flight conditions are quantified using Romax® software. To validate the approach, the results are correlated to in flight measured strains on a gearbox housing. As another validation point, a separate Romax model is correlated to quasi-static strain measurements on a UH-60 planetary carrier plate with the transmission subjected to torque and hub moments in a lab. This work enables greater fidelity in the design and analysis of helicopter transmissions. It may help to improve reliability and reduce weight in current and future designs. The work also presents an application of system-level modelling and analysis. Instead of considering the various components in isolation with simplified boundary conditions, the analysis considers the full system and how the components interact across connection points such as splines, bearings, and gear meshes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.204

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 head0.005
GPT teacher head0.240
Teacher spread0.235 · 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 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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