MétaCan
Menu
Back to cohort
Record W2435597037 · doi:10.1109/tasc.2016.2580564

AC Loss Analysis of Central Solenoid Model Coil for China Fusion Engineering Test Reactor

2016· article· en· W2435597037 on OpenAlexaff
Wei Zhou, Jin Fang, Бо Лю, Xinyu Fang, Yanchao Liu, Chenxi Jia

Bibliographic record

VenueIEEE Transactions on Applied Superconductivity · 2016
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsUniversity of Victoria
FundersUniversity of TwenteVictoria University of Wellington
KeywordsSolenoidElectromagnetic coilConductorNuclear engineeringPower (physics)Nuclear magnetic resonanceMagnetSuperconducting magnetNiobium-tinMagnetic fieldComputer scienceMaterials sciencePhysicsMechanical engineeringElectrical engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Central solenoid (CS) is one of the most important components of China Fusion Engineering Test Reactor (CFETR), which is designed by the China National Integration Design Group. It is a time-consuming work such a complex CS magnet is designed; therefore, it is necessary to design central solenoid model coil (CSMC) and to determine its electromagnetic properties beforehand. In this paper, we present a CSMC that is suitable for studying the electromagnetic properties. The design parameters, the magnetic field distribution, and the ac loss of the CSMC were carefully considered for future baseline design. Based on the piecewise-linear method, the peak power and the time bucket losses were calculated. Furthermore, the ac loss of the different types of Nb3Sn conductor-based coil was obtained and compared. Finally, the proposed test method for measuring the ac loss of the CSMC was described.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Applied SuperconductivitySame topicSuperconducting Materials and ApplicationsFrench-language works237,207