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Record W3093854309 · doi:10.4050/f-0076-2020-16371

Helicopter Flight Test Evaluation of an Actively Stabilized External Slung Load

2020· article· en· W3093854309 on OpenAlexaboutno aff
Marc Alexander, Eitenne Perron, Gregory Craig, David Rancourt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
Fundersnot available
KeywordsAirframePendulumActuatorSimulationControl theory (sociology)Computer scienceEngineeringAerospace engineeringMechanical engineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

The National Research Council of Canada and Université de Sherbrooke performed flight testing of an Actively Stabilized Slung Load on the NRC Bell 206 Research Aircraft. Hover, Attitude Capture, NRC designed Lateral Precision Hover, and Frequency Sweep mission tasks were performed for bare airframe and slung load aircraft configurations. The load mass ratio was 0.12 while the slung load pendulum mode was 1.3 rad/sec at a damping ratio of 0.2 for the 40-pound per active tether saturation load system setting. Time domain response indicated that the load remained controllable with damped and underdamped behaviors. Frequency domain analyses confirmed pilot comments indicating HQR 4 handling qualities ratings for bare airframe and stable slung load behavior. This rating degraded to HQR 5 for task execution with slung load oscillation. Pilot workload was due to lateral cycle input requirements of 2 to 3 inch amplitudes at 1 to 2 Hz frequency. Operationally, the coincidence of pilot inputs with active tether induced airframe and short period modes led to high compensation requirements for lateral axis tasks under study. The complexity of active tether management, an actuator failure, and telemetry faults represented system deficiencies. Comparable bare airframe and slung load configuration task execution results indicate the magneto-rheological actuation system’s potential to improve slung load mission task 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.987

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)0.0010.000

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 head0.025
GPT teacher head0.256
Teacher spread0.231 · 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
Published2020
Admission routes1
Has abstractyes

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