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Record W4366674902 · doi:10.4050/f-0077-2021-16875

Comparative Flight Test Evaluation of Passive and Active External Slung Load Dynamics

2021· article· en· W4366674902 on OpenAlexaffabout
Marc Alexander

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsUniversité de SherbrookeNational Research Council Canada
Fundersnot available
KeywordsAirframeSimulationDragPendulumActive loadControl theory (sociology)Computer scienceEngineeringAerospace engineeringMechanical engineeringControl (management)Electrical engineering

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, Passive Water-Filled Barrel and Active Slung Load configurations. Load Mass Ratios of 0.12 with pendulum modes of 1.3 rad/sec were identified for both configurations. Time domain response indicated that both loads remained controllable under excitation by pilot gain, winds, and helicopter-load mode coalescence. Frequency domain analyses confirmed pilot comments indicating HQR-4 handling qualities ratings for bare airframe and stable load behaviors. This degraded to HQR-5 for task execution with load oscillation effects rated at pilot induced oscillation tendency of PIO/T-4. Barrel load HQR degradation related to load inertial and surface area drag effects versus active tether feedback and roll-pitch actuation disharmony of the Active load. Though not optimized, this load's swing behavior (consistent period and rate) can aid in management of aircraft rate response and pilot compensation. Overall, comparable passive and active test results indicate potential for magneto-rheological actuation 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 score0.433
Threshold uncertainty score0.238

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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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
Published2021
Admission routes2
Has abstractyes

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