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Record W3199285557 · doi:10.1109/tps.2021.3109170

A New Hybrid Pulsed Power Circuit Topology for Gas Laser Applications

2021· article· en· W3199285557 on OpenAlexaff
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
Fundersnot available
KeywordsThyratronPulsed powerTransformerElectronic circuitElectrical engineeringComputer scienceTopology (electrical circuits)Pulse generatorElectronic engineeringVoltageEngineering

Abstract

fetched live from OpenAlex

A major decision surrounding the design of pulsed power circuits for gas lasers is the choice between a thyratron and solid-state switches. The thyratron is a mature device technology whose cost is increasing. The ratings of solid-state switches still do not match those of a thyratron. Pulse transformer and pulse compression circuits must be added to overcome the lower ratings of solid-state switches leading to a higher parts count and cost. This article proposes a new solid-state pulse power topology called the hybrid circuit topology to address the parts count and cost issue. The hybrid circuit makes use of a combination of capacitive and inductive energy storage to reduce the time required to transfer the energy from the transformer primary to the secondary side. This ultimately reduces the quantity of magnetic material used in the pulse compression circuit.

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)0.000
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.233
Teacher spread0.217 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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