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Study of Supersonic Flows Interacting with DC Discharge Plasma on a Diamond Airfoil

2018· article· en· W2893370549 on OpenAlexaboutno aff
Tetsuo Koike, Kazumi TSUNODA

Bibliographic record

VenueThe Proceedings of Conference of Kanto Branch · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPlasma actuatorSupersonic speedMach numberPlasmaAirfoilBoundary layerWind tunnelAirflowBrush dischargeMaterials scienceNozzleMechanicsDischarge coefficientChemistryDielectric barrier dischargePhysicsThermodynamics

Abstract

fetched live from OpenAlex

This paper describes the experimental results of the influence of the DC discharge plasma generated on the diamond airfoil in a Mach 1.41 airflow. The experiments were carried out in an indraft supersonic wind tunnel installed with the Laval nozzle. The discharge was generated between a pair of electrodes mounted on the airfoil surface. Current and voltage measurements indicated that the discharge became unstable at low power consumption. When the backpressure ratio of Pb / P0 is in the range of 0.1 to 0.13, it was possible to generate plasma with 79.5 W of discharge power at 0.019 s intervals. In the case of 0.23 < Pb / P0 < 0.26, the discharge period reduced to 0.013 s and the power consumption decreased to 50.7 W. From the visualization image of the flow field, the expansion wave from the entire plasma emission region was observed, and slight increase in the boundary layer thickness was seen at the downstream of the electrode. In order to detect the effects of the plasma on the supersonic airflow, a total pressure in the plasma discharge region was measured. It was suggested that the total pressure in the plasma discharge might be higher than without discharge but confirming of the reproducibility is necessary in the future investigation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.622

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

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