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Record W1989006127 · doi:10.1115/detc2007-34656

Evaluation of a Low-Noise Formate Spiral-Bevel Gear Set

2007· article· en· W1989006127 on OpenAlexaff
David G. Lewicki, Ron L. Woods, Faydor L. Litvin, Alfonso Fuentes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsBevel gearNoise (video)BevelPinionSpiral bevel gearSpiral (railway)VibrationEngineeringStructural engineeringAcousticsMaterials scienceComputer scienceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Studies to evaluate low-noise Formate spiral-bevel gears were performed. Experimental tests were performed on the OH-58D helicopter main-rotor transmission in the NASA Glenn 500-hp Helicopter Transmission Test Stand. Low-noise Formate spiral-bevel gears were compared to the baseline OH-58D spiral-bevel gear design, a high-strength design, and previously tested low-noise designs (including an original low-noise design and an improved-bearing-contact low-noise design). Noise, vibration, and tooth strain tests were performed. The Formate design showed a decrease in noise and vibration compared to the baseline OH-58D design, and was similar to that of the previously tested improved-bearing contact low-noise design. The pinion tooth stresses for the Formate design significantly decreased in comparison to the baseline OH-58D design. Also similar to that of the improved bearing-contact low-noise design, the maximum stresses of the Formate design shifted toward the heel, compared to the center of the face width for the baseline, high-strength, and previously tested low-noise designs.

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)0.001
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 score0.138
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.258
Teacher spread0.238 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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