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Record W2166321866 · doi:10.1109/icelmach.2012.6350208

Modeling and dimensioning of High Voltage pulse transformers for klystron modulators

2012· article· en· W2166321866 on OpenAlexaff
P. Viarouge, Davide Aguglia, Carlos Augusto Paiva da Silva Martins, J. Cros

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsKlystronDimensioningTransformerHigh voltageElectrical engineeringElectronic engineeringVoltagePulsed powerComputer scienceTopology (electrical circuits)EngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

In linear particle accelerators, klystrons operating in synchronized pulsed mode are used for RF power production and their power supply is provided by modulators that are high voltage and high power pulse generators. One solid state topology of klystron modulator is using a monolithic High Voltage pulse transformer. Future electron-positron linear colliders like the Compact Linear Collider (CLIC) will need a huge number of klystron modulators with tight dynamical specifications and high efficiency for minimizing their power consumption. With such new complex specifications, the feasibility of the modulator topology needs the development of an accurate methodology for the design of High Power & High Voltage Pulse Transformers. This paper presents the first development steps of an optimal design environment of these devices in terms of modeling & dimensioning tools and its applications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0010.001
Open science0.0010.000
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 head0.018
GPT teacher head0.285
Teacher spread0.267 · 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 designSimulation or modeling
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

Citations12
Published2012
Admission routes1
Has abstractyes

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