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Record W3212648766 · doi:10.1109/tps.2021.3123595

The Hybrid Pulsed Power Circuit Modeled as a Double-Discharge Circuit for Gas Laser Applications

2021· article· en· W3212648766 on OpenAlexafffund
Harpreet Singh Grover, F.P. Dawson

Bibliographic record

VenueIEEE Transactions on Plasma Science · 2021
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPulsed powerCapacitorElectronic circuitTopology (electrical circuits)Marx generatorMaterials scienceIgnition systemElectrical engineeringElectric discharge in gasesVoltageLaserComputer sciencePhysicsOpticsEngineering

Abstract

fetched live from OpenAlex

Long pulse gas lasers utilizing double-discharge circuits are different from the single-short pulse gas lasers in that they do not require all of the desired energy to be deposited into the peaking capacitors prior to ignition of the main gap. Instead, the majority of the energy is held in another capacitor that is then discharged in the form of a longer duration discharge pulse output compared with that of single-short pulse lasers. Extended stability of the discharge is typically achieved by igniting the main gap discharge at higher ignition voltages. Several double-discharge circuit topologies exist in the literature. However, each topology has its own disadvantages primarily centered around the components needed and the resulting complexity. In this article we introduce the application of the new hybrid circuit pulsed power topology as a double-discharge circuit with high ignition voltages. The hybrid circuit pulsed power topology was originally published by the authors of this article in their previous publication titled “A New Hybrid Pulsed Power Circuit Topology for Gas Laser Applications.” The advantage of the hybrid circuit as a double-discharge circuit is that it does not require any special device [e.g., semiconductor opening switch (SOS) diode] and can use commonly available components.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.237
Teacher spread0.216 · 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.

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

Citations3
Published2021
Admission routes2
Has abstractyes

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