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Record W4382052314 · doi:10.4050/f-0079-2023-18122

Helicopter Aerodynamic Loading in the Airwake of a Moving Ship

2023· article· en· W4382052314 on OpenAlexaffabout
Nicholas Fernandez, Alanna Wall, I. Owen, Mark White, Richard Lee, Weixing Yuan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAerodynamicsThrustHarmonicsRotor (electric)Motion (physics)Helicopter rotorMarine engineeringResponse amplitude operatorAeroelasticityShip motionsEngineeringAerospace engineeringStructural engineeringComputer scienceMechanical engineeringHull

Abstract

fetched live from OpenAlex

This paper presents an analysis of the unsteady aerodynamic loading of a helicopter immersed in the airwake of a generic destroyer undergoing several ship motion types: static, sinusoidal pitching, regular 2-DOF, and realistic irregular 3-DOF ship motions. The study was a collaborative effort between the National Research Council Canada and the University of Liverpool, where each organization have applied their respective modeling approach (whether by simulation or experiment). It was shown that whilst there were only marginal differences in the mean helicopter loads for each motion type, the effects of ship motion are more apparent in the RMS loads, and the instantaneous load and air-velocity spectra. Dominant peaks in the thrust load spectra, as well as subsequent second and third harmonics, are shown at the ship motion frequency for sinusoidal pitching and regular 2-DOF ship motions, which have been attributed to the interaction between the turning rotor and the moving ship. This analysis provides a foundation for understanding the relationships between ship airwake and helicopter loading, and for extending that understanding to impacts on flight operations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.194

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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 designObservational
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

Citations2
Published2023
Admission routes2
Has abstractyes

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