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Record W2869522375 · doi:10.4050/f-0074-2018-12863

Conceptual Design and Analysis of Hybrid Composite Power Gearing in a Fielded Drive System Configuration

2018· article· en· W2869522375 on OpenAlexaff
Gilbert Morales, Cody Anderson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsPower (physics)Conceptual designComposite numberComputer scienceEngineeringAutomotive engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Rotorcraft drive systems are continuously assessed for opportunities of improvement, with increased power at reduced weight as a primary motivation. A key area of interest is the use of composite materials in drive system configurations. Composites can be significantly lighter than metallic components currently in use. Rotorcraft transmissions can leverage weight reduction with hybrid composite gearing that incorporates metallic features (for high load elements such as the gear teeth), while using composites for lower stress features. While the weight benefits of hybrid composite gearing are significant, specific challenges require attention before the benefits can be realized in an advanced vertical lift drive system. This research effort, conducted under the Revolutionary Vertical Lift Technology (RVLT) project, identified an advanced drive system configuration, developed conceptual designs with hybrid composite power gearing, identified the technical challenges with hybrid composite power gearing, and provided recommendations for overcoming specific, technical challenges. The integration of composite materials in advanced drive systems proves to be a promising approach with improving performance.

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: none
Teacher disagreement score0.627
Threshold uncertainty score0.218

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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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
Published2018
Admission routes1
Has abstractyes

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