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Record W4401137431 · doi:10.18280/mmep.110709

Numerical Modeling of a Dielectric Discharge Plasma Actuator Using Local Energy Approximation for Application to Flow Control

2024· article· en· W4401137431 on OpenAlexvenueno aff
Moustapha Ouali, Youssef Lagmich

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPlasma actuatorDielectric barrier dischargeDielectricFlow control (data)PlasmaMechanicsActuatorFlow (mathematics)Materials scienceEnergy (signal processing)Environmental scienceControl theory (sociology)Control (management)Electrical engineeringPhysicsEngineeringComputer scienceOptoelectronicsTelecommunications

Abstract

fetched live from OpenAlex

The primary benefit of the DBD plasma actuator is its macroscopic stability, which makes it one of the most frequently cited plasma actuators in the academic literature.This study examined the potential of dielectric barrier discharge (DBD) plasma actuators for flow control.The plasma actuator is composed of an exposed electrode, a covered electrode, and a dielectric layer that separates the electrodes.A parametric analysis was performed to explore the impact of macroscopic variables, such as pressure, applied voltage, and relative permittivity of the dielectric, on the transfer of momentum between particles affected by Coulomb forces.The results underscore the crucial influence of the dielectric material on ion velocity.The observed trend demonstrates a clear connection between the increase in the relative permittivity of the dielectric material and the corresponding increase in ion velocity.This emphasises the significant importance of choosing the correct dielectric material, particularly in situations where ion velocity is critical, such as in the precise regulation of airflow around an aerodynamic profile.

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: Methods · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.867

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 head0.011
GPT teacher head0.195
Teacher spread0.185 · 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
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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Same venueMathematical Modelling and Engineering ProblemsSame topicPlasma and Flow Control in AerodynamicsFrench-language works237,207