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Record W6982190120

High Frequency Raster Magnets Design for the ARIEL Electron Target Station at TRIUMF

2022· dissertation· en· W6982190120 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2022
Typedissertation
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsnot available
Fundersnot available
KeywordsEddy currentMagnetElectromagnetSolenoidElectrical conductorSuperconducting magnetQuadrupole magnetBeamlineElectromagnetic coilBeryllium
DOInot available

Abstract

fetched live from OpenAlex

TRIUMF, Canada’s nuclear and particle physics research laboratory is currently in development of an Advanced Rare Isotope Facility (ARIEL) that will contain a newly designed electron target station. The target at this station is susceptible to destruction from instantaneous spot heating of the beam. To mitigate this, a raster system consisting of two AC electromagnets was proposed. The two magnets will work in tandem, vertical and horizontal, bending to produce raster patterns at 10 kHz. Since complex patterns consist of harmonics higher than the fundamental frequency, a design frequency of 100 kHz was chosen. AC current causes eddy currents which lead to the skin effect, causing high frequency current to concentrate on the outside of the conductor. To address this, a conductor diameter smaller than the skin depth at the given frequency must be chosen. This led to the choice of litz wire consisting of 400 insulated strands for the conductor. The radiation resistance insulation ethylene tetrafluoroethylene (ETFE) was chosen for these conductors and a 3D printed polyethylene sulfide (PPS) will be used for the coil bobbins. The effects of eddy currents were eliminated from the core material by choosing ferrite, an amorphous material consisting of iron-oxide crystals. Simulations were completed to ensure a homogenous magnetic field in the region of the beam, and the subsequent pole profile was determined. Lastly, a metalized ceramic beampipe is used to integrate with the existing beamline and allow for discharge of any static buildup on the inner surface of the beampipe due to the electron beam.

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: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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