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Record W4404293952 · doi:10.1109/tia.2024.3496832

Circuit Topologies and Techniques for High Voltage Plasma Ignition

2024· article· en· W4404293952 on OpenAlexaff
Harpreet Singh Grover, F.P. Dawson

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIgnition systemNetwork topologyVoltageElectrical engineeringPlasmaPlasma chemistryMaterials scienceCapacitorComputer scienceElectronic engineeringEngineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

In a pulsed plasma (discharge) application requiring sub-microsecond pulses (such as gas lasers), there are major advantages to ignite or breakdown the discharge at higher voltages (higher tens of kVs). This is especially true if the pulse is only a few tens of nanoseconds in duration. The advantage is, primarily, higher peak power through the gap discharge. Higher peak powers with a shorter pulse width in most cases favor increased efficiencies in the kinetic processes in the plasma, which, for instance, in gas lasers leads to high laser energy outputs for a given energy input. However, the challenge is to achieve shorter times to charge the peaking capacitor. Shorter charging times can be achieved using pulse compression stage(s) but are limited by the stray loop inductance. Achieving a higher voltage ignition level thus remains a major challenge in the pulse power industry. This paper proposes a new concept: the use of a magnetic switch to be inserted between the peaking capacitor and the gap to block a part of the peaking capacitor's rising voltage. This shortens the time during which an applied voltage is seen by the gap thus enabling it to be ignited at high voltages.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.691

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 head0.020
GPT teacher head0.248
Teacher spread0.227 · 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
Published2024
Admission routes1
Has abstractyes

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