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Record W2738103803 · doi:10.4050/f-0073-2017-12041

Evaluation of Aircrew Whole-Body Vibration and Mitigation Solutions for Helicopter Flight Engineers

2017· article· en· W2738103803 on OpenAlexaff
Upekha Yapa, Andrew Price, Viresh Wickramasinghe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAircrewAeronauticsAerospace engineeringVibrationWhole body vibrationEngineeringAutomotive engineeringComputer scienceMarine engineeringEnvironmental scienceAcousticsPhysics

Abstract

fetched live from OpenAlex

Flight testing has been performed on a Bell-205 helicopter to investigate aircrew whole-body vibration (WBV) exposure levels on a rag and tube Flight Engineer (FE) seat in accordance with ISO2631 and MIL-STD1472 standards. Results show that the helicopter cabin vibration is dominated by the N/rev harmonics of the two-bladed main rotor speed. The aircrew WBV levels vary significantly depending on the flight conditions; the highest WBV levels occur at high speed level flight conditions. With reference to the ISO2631 and MIL-STD-1472 guidelines, the aircrew WBV exposure level on the standard FE seat in the entire tested flight profile is qualitatively rated as "uncomfortable". Limiting the maximum duration of such missions to 1 hour would ensure compliance with the limit of ISO2631 vibration health and risk guideline "Caution Zone". It is also noted that the use of gunner seat insert cushion for combat missions can lead to a significant increase in the aircrew WBV levels. However, the use of selected carry-on cushion pads can provide an effective WBV mitigation to the aircrew in the majority of flight conditions. Further occupant WBV tests on a human rated mechanical shaker table also verified that selected carryon seat cushion pads are also effective in the mitigation of occupant WBV levels on the Bell-412 helicopter.

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)0.001
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 score0.661
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.056
GPT teacher head0.373
Teacher spread0.317 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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