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Record W4297952359 · doi:10.1615/tsfp7.880

COMPARATIVE ANALYSIS ON SINGLE DIELECTRIC BARRIER DISCHARGE PLASMA ACTUATOR MODELS

2011· article· en· W4297952359 on OpenAlexaff
Denis Palmeiro, Philippe Lavoie

Bibliographic record

VenueProceeding of Seventh International Symposium on Turbulence and Shear Flow Phenomena · 2011
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPlasma actuatorActuatorDielectric barrier dischargeVoltageMechanicsPlasmaDielectricRange (aeronautics)ScalingControl theory (sociology)Materials scienceExcitationEngineeringPhysicsComputer scienceElectrical engineeringMathematicsGeometryOptoelectronics

Abstract

fetched live from OpenAlex

Single-dielectric-barrier-discharge (SDBD) plasma actuators have shown much promise as an actuator for active flow control. Proper design and optimization of plasma actuators requires a model capable of accurately predicting the induced flow for a range of geometrical and excitation parameters. A number of models have been proposed in the literature, but have primarily been developed in isolation on independent geometries, frequencies and voltages. Many of these models rely on parameters that have been calibrated for one specific geometry. This study presents a comparison of four popular plasma actuator models over a range of actuation parameters for three different actuator geometries typical of actuators used in the literature. The results show that the hybrid model of Lemire & Vo (2011) is the only model capable of predicting the appropriate trends in how the body force and induced velocity change for different geometries. Additionally, it is the only model that exhibits a non-linear velocity scaling with voltage with a power law relationship of exponent 1.5, compared to the empirical result of a 3.5 power law found in the literature.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.895

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 head0.022
GPT teacher head0.220
Teacher spread0.198 · 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 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

Citations5
Published2011
Admission routes1
Has abstractyes

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