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Record W4403121690 · doi:10.4050/f-0072-2016-11429

The Bell 525's Smooth Ride

2016· article· en· W4403121690 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The Bell 525 Relentless is designed to fly farther and faster than any previous Bell commercial helicopter. The increase in flight time means that vibration and internal noise are more significant elements to crew and passenger comfort. To address crew exposure to these factors, the 525 employs a three-tier strategy for reducing vibration and structure-borne acoustics. The approach first reduces the transmissibility of source vibrations from entering the airframe. Secondly, the airframe structure natural modes are desensitized from the rotor excitation frequencies. Third, remaining vibrations are treated with vibration reduction equipment. While this strategy was successfully used in the design of the Bell 429, the 525 Relentless takes this approach to the next level through lessons learned from the 429, improvements in the pylon design, and incorporating technology advancements in active vibration reduction. With first flight of the 525 Relentless in July 2015, envelope expansion through 200 knots airspeed has demonstrated the success of this three-tier strategy with vibration levels that set the standard for the industry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0750.016

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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