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Record W2093863777 · doi:10.1063/1.1808773

Linear feedback stabilization of laminar vortex shedding based on a point vortex model

2004· article· en· W2093863777 on OpenAlexaff
Bartosz Protas

Bibliographic record

VenuePhysics of Fluids · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsMcMaster University
FundersÉcole Supérieure de Physique et de Chimie Industrielles de la Ville de Paris
KeywordsLinear-quadratic-Gaussian controlPhysicsLaminar flowControl theory (sociology)VortexWakeVortex sheddingMechanicsReynolds numberFlow (mathematics)ControllabilityTurbulenceClassical mechanicsOptimal controlMathematicsApplied mathematicsComputer scienceMathematical optimization

Abstract

fetched live from OpenAlex

In this paper we use the Föppl point vortex system as a reduced-order model for stabilization of the steady symmetric solution in an unstable laminar wake. The downstream location of the Föppl vortices is chosen so as to produce the same recirculation length as in the actual flow at a given Reynolds number. When the cylinder rotation is used as flow actuation, the linearized Föppl system is shown to be stabilizable, but not controllable. With centerline velocity measurements as the system output, the linearized Föppl model is also shown to be fully observable. The Linear-Quadratic-Gaussian (LQG) control design is performed based on the linearized Föppl system which has only four degrees of freedom. Computational results show that thus designed LQG compensator stabilizes the stationary solution of the nonlinear Föppl system. When applied to an actual cylinder wake at Re=75, the LQG compensator stabilizes the downstream region of the flow. Possibilities and limitations of flow control strategies based on point vortex systems as reduced-order models are discussed.

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 score0.003
Threshold uncertainty score0.005

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 communication0.0000.000
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 head0.019
GPT teacher head0.259
Teacher spread0.240 · 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

Citations63
Published2004
Admission routes1
Has abstractyes

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