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Record W4312952876 · doi:10.1115/fedsm2022-87953

Comparative Analysis of Step Change and Reduced Slew Rate Input on the Boundary Layer Response to Forcing by an Array of Plasma Actuator Vortex Generators

2022· article· en· W4312952876 on OpenAlexaff
Michael Varacalli, Hossein Khanjari, Ronald Hanson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsYork University
Fundersnot available
KeywordsMechanicsActuatorControl theory (sociology)Plasma actuatorBoundary layerLaminar flowPhysicsVortexEngineeringPlasmaComputer science

Abstract

fetched live from OpenAlex

Abstract In this study, simulations of the laminar boundary layer response to forcing by an array of streamwise oriented plasma actuators are performed. The objective of this study is to evaluate a method to mitigate the non-minimum phase behaviour of the flow that occurs during the step response to input by the actuators. It was recently shown that when the actuators are abruptly started the non-minimum phase response of the wall shear and near-wall disturbance velocity is detected downstream of the actuators. This was shown to be caused by a secondary flow generated between the wall and the front of the tilted vortex structure that is advected downstream [1]. In the present study the step input response is compared to gradually applied inputs. For the step response, the actuators are abruptly activated for 0.2 s followed by a rest period for the flow to return to the undisturbed Blasius condition. For the gradually applied input, the body force of the actuators increases linearly from zero for either 0.005 s, 0.01 s, 0.02 s and 0.04 s to the same maximum value of the step, which simulates a reduced slew rate applied to the physical actuator. The value is held constant and then linearly decreases over a total duration of 0.2 s. It is shown the that inverse response remains for the gradually applied input. However, the peak magnitude is less for the lower ramp rates and the overall response appears more damped.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.247
Teacher spread0.219 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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