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Record W1968644649 · doi:10.1063/1.4739231

Physical characteristics of gliding arc discharge plasma generated in a laval nozzle

2012· article· en· W1968644649 on OpenAlexaboutno aff
Shizhou Lu, Xiao-dan Sun, Xiangzhe Li, Jun Yan, C. M. Du

Bibliographic record

VenuePhysics of Plasmas · 2012
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleWaveformVolumetric flow rateElectric arcMechanicsNitrogenOxygenArc (geometry)PhysicsVoltagePlasmaCurrent (fluid)Flow (mathematics)Analytical Chemistry (journal)Atomic physicsRange (aeronautics)ThermodynamicsMaterials scienceElectrodeChemistryComposite materialMechanical engineeringNuclear physics

Abstract

fetched live from OpenAlex

The dynamic behavior of gliding arc discharge generated in a Laval nozzle has been investigated by electrical diagnostics and a high-speed camera. The results show that the voltage waveform keeps the initial shape as the gas flow rate is small, while it becomes less stable with increasing flow rate. During the first half of a cycle, the voltage rises and after that it decreases. In nitrogen and oxygen, the break down voltage for the arc is between 3.3 and 5.5 kV, while it is between 3.3–7.5 kV in air. The waveform of current I remains almost stable; and for nitrogen and oxygen, the maximum value of current I is between 0.28 and 0.46 A. With increasing flow rate, the power consumption in air first increases and then decreases; it remains in the range of 110–217 W, and gradually increases in nitrogen and oxygen. The power consumption in oxygen is lower than that in nitrogen; the input of the energy density decreases with increasing flow rate for all the three gases. The development of the arc is tracked and recorded by a high-speed camera. The cycle is stable at 10 ms for flow rates up to 1 m3 h−1. At a higher flow rate, the cycle becomes unstable.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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 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

Citations32
Published2012
Admission routes1
Has abstractyes

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