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Record W4399810719 · doi:10.23977/jeis.2024.090213

Research on Large-Signal Simulation of Helix-Loaded Azimuthally Periodic Circular Waveguide for 140-GHz TWT

2024· article· en· W4399810719 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Electronics and Information Science · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsnot available
Fundersnot available
KeywordsSIGNAL (programming language)Helix (gastropod)OpticsWaveguidePhysicsCircular polarizationAcousticsElectrical engineeringMaterials scienceEngineeringComputer scienceMicrostripGeology

Abstract

fetched live from OpenAlex

To address the issues of low output power and focusing difficulties encountered by conventional helix traveling wave tube (TWT) in the short millimeter wave band, a novel helix-loaded azimuthally periodic circular waveguide TWT (HLAP-CW TWT) is proposed in this paper. The novel structure consists of helixes that are arranged periodically about the axis of the conventional circular waveguide. And the N helixes share a single electron beam with a large current value in axial. In this paper, the specific design scheme for the HLAP-CW TWT of 140 GHz is proposed. The dispersion characteristics of the HLAP-CW are studied by HFSS in order to determine the operation voltage of the beam-wave interaction. Furthermore, the 3-D particle-in-cell (PIC) simulations are carried out with the electron beam voltage U=3758V and the electron beam current I=0.25A. In the operation frequency range from 120GHz to 154GHz, the output powers of the HLAP-CW TWT all exceeds 55W with the input power maintained 50mW. The maximum corresponding gain and the highest beam-wave interaction efficiency are 25.98dB and 8.43%, respectively. Therefore, the HLAP-CW TWT is a millimeter-wave amplifier with a wide bandwidth and a large output power, which provides a new solution of the current challenges faced by millimeter-wave TWT.

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)0.005
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.400
Teacher spread0.357 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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