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Record W3201107510 · doi:10.1038/s41598-021-98238-8

Development of a novel approach for construction of high gradient braze-free S-band cavities

2021· article· en· W3201107510 on OpenAlexaff
Mahdi Aghayan, S. Farhad Masoudi, Farshad Ghasemi, Walter Wuensch, H. Shaker

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsCanadian Light Source (Canada)
Fundersnot available
KeywordsBrazingCopperTemperature gradientLinear particle acceleratorMaterials scienceDevelopment (topology)SofteningMechanical engineeringComputer scienceMetallurgyStructural engineeringComposite materialPhysicsEngineeringBeam (structure)Mathematics

Abstract

fetched live from OpenAlex

Vacuum breakdown is one of the main limitations to the operating accelerating gradient in radio frequency linear accelerators. Recent studies of copper cavities have been shown that harder copper conditions more quickly and can reach higher accelerating gradients than soft copper cavities. Exploiting this advantage requires the development of assembly methods that do not involve the copper-softening high-temperature heating cycles that are used in for example bonding and brazing. A shrink-fit method, which was already implemented successfully in the operation the IPM linac, is proposed for the construction high-gradient test S-band standing wave structure operating at 2998.5 MHz. The three cells cavity is designed to have a maximum gradient in the middle cell that is twice that of the adjacent cells. Mechanical considerations relating to the shrink-fit construction method have been performed using Ansys. To validate the simulations and ensure the feasibility of construction by shrink-fit method, a sample cavity was constructed and cold tests was performed.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.315

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 head0.218
Teacher spread0.198 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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