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Record W2333505503 · doi:10.2514/6.2013-1715

Blade Sailing Phenomenon Modeling for Feedback Control

2013· article· en· W2333505503 on OpenAlexaff
Mohammad R. Riazi, Fred F. Afagh

Bibliographic record

Venue54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsControl theory (sociology)AeroelasticityNonlinear systemRotor (electric)AerodynamicsHelicopter rotorDiscretizationBlade (archaeology)Galerkin methodFlappingComputer scienceBlade element theoryController (irrigation)ElevatorActuatorEngineeringPhysicsAerospace engineeringStructural engineeringMathematicsMechanical engineeringMathematical analysisControl (management)

Abstract

fetched live from OpenAlex

In this paper, a reduced control-centric model of a particular behavior associated with maritime helicopter rotor systems, known as Blade Sailing Phenomenon (BSP), is developed. BSP is a transient aeroelastic phenomenon described by the large undesired flapping motion of the helicopter rotor blades during low rotor speeds. The developed model utilizes the Unified Airloads Model to capture the aerodynamic loads, the Intrinsic Nonlinear Beam Model to capture the structural behavior of the blade, and the Integrally Actuated Twist (IAT) for actuation. The governing partial-differential equations of motion together with the blade structural displacements as the output of interest are then discretized in the space domain using the Galerkin Spectral Discretization Method. The resultant nonlinear ordinary-differential equations are in the standard form of nonlinear systems that is highly desirable for designing a feedback controller to counter the BSP.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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Same venue54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials ConferenceSame topicAeroelasticity and Vibration ControlFrench-language works237,207