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Record W3009949174 · doi:10.1109/tasc.2020.2978464

Topology Optimization of the Pole Shape in Passive Magnetic Channel Using MMA Method

2020· article· en· W3009949174 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Applied Superconductivity · 2020
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsTRIUMF
FundersNational Natural Science Foundation of China
KeywordsMagnetic fieldCyclotronPhysicsMagnetostaticsTopology (electrical circuits)Computational physicsAsymptoteFinite element methodSuperconducting magnetGeometryMathematicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Passive magnetic channel is a kind of beam focusing elements in a cyclotron. It consists of several soft iron bars that are magnetized by the main field in a cyclotron. In this paper, we proposed a topology optimization method to design the pole shape in passive magnetic channel, this method does not require any fixed geometry pattern or initial design. The nonlinear static magnetic finite-element analysis model is used to calculate the objective magnetic field function. Persuade iron material with variable density is used to describe the iron distribution during the iteration. Method of Moving Asymptotes (MMA) is used to optimize the control variable of iron density distribution on magnetic channel cross-section. In three numerical examples, magnetic channels for a 250 MeV superconducting cyclotron is provided, where the design goal is to provide the given magnetic field gradient and bending angle. The relationship between the design goal and the pole shape pattern is discussed. It reveals that magnetic channel pattern with only 2 iron bars is possible for some design goals.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.235
Teacher spread0.213 · 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