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Record W2272702913 · doi:10.1017/s0001924000001780

Rotorcraft simulation modelling and validation for control law design

2007· article· en· W2272702913 on OpenAlexaff
Binoy J. Manimala, Daniel Walker, Gareth D. Padfield, Mark Voskuijl, Arthur Gubbels

Bibliographic record

VenueThe Aeronautical Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsInflowControl theory (sociology)Rotor (electric)Transient (computer programming)Distortion (music)Finite element methodDamperEngineeringLine (geometry)Helicopter rotorSimulationComputer scienceControl engineeringStructural engineeringMechanical engineeringPhysicsMechanicsControl (management)Mathematics

Abstract

fetched live from OpenAlex

Abstract This paper describes the development and validation of a high fidelity simulation model of the Bell 412 helicopter for handling qualities and flight control investigations. The base-line model features a rigid, articulated blade-element formulation of the main rotor, with flap and lag degrees of freedom. The Bell 412 HP engine/governor dynamics are represented by a second-order system. Other key features of the base-line model include a finite-state dynamic inflow model and lag damper dynamics. The base-line model gives excellent agreement with flight-test data over the speed range 15-120kt for on-axis responses. Prediction of off-axis responses is less accurate. Several model enhancement options were introduced to obtain an improved off-axis response. It is shown that the pitch/roll off-axis responses in transient manoeuvres can be improved significantly by including wake geometry distortion effects in the Peters-He finite-state dynamic inflow model.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations26
Published2007
Admission routes1
Has abstractyes

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