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Record W2729568762 · doi:10.4050/f-0070-2014-9664

Experimental Characterization of a Small-Scale Tailboom with Fluidic Flexible Matrix Composite Tubes

2014· article· en· W2729568762 on OpenAlexaff
Kentaro Miura

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsComposite numberFluidicsCharacterization (materials science)Matrix (chemical analysis)Materials scienceScale (ratio)Composite materialComputer scienceEngineeringNanotechnologyPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Rotorcraft tailbooms vibrate due to excitation from the rotors, separated flow behind the rotor hub, vehicle maneuvers, and wind gusts. They also typically have low inherent structural damping. This vibration leads to driveline component wear, structural fatigue, and passenger discomfort. Fluidic Flexible Matrix Composite (F2MC) tubes, a new class of passive vibration treatments, are attached to a representative tailboom structure and experimentally tested. Each F2MC tube consists of a stainless steel mesh surrounding a rubber tube. The mesh and tube are independently fastened to end fittings that mechanically connect and fluidically seal the F2MC tubes, respectively. Two tubes are mounted to the top and bottom of the tailboom and interconnected with a fluidic circuit that can be pressurized. Tests are performed to measure the fluid volume pumped by the F2MC tubes when the tailboom bends, and the tailboom displacement in response to F2MC tube pressurization. Experimental results demonstrate that the F2MC tubes can actuate tailboom bending and pump fluid. The actuation and fluid pumping results agree well with theoretical predictions. Numerical simulations, based on a previously developed model, have indicated potential for F2MC tubes to provide an efficient means of introducing useful levels of damping into tailboom structures. Characterization of the dynamic performance is ongoing.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.332

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 head0.004
GPT teacher head0.185
Teacher spread0.180 · 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
Published2014
Admission routes1
Has abstractyes

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