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Record W2942459640 · doi:10.1109/tasc.2019.2908599

Impact of Current Flow Diverter on Innovative HTS Tape Architectures for DC Fault Current Limitation at Electric Fields up to 150 V/m

2019· article· en· W2942459640 on OpenAlexaff
Christian Lacroix, Frédéric Sirois

Bibliographic record

VenueIEEE Transactions on Applied Superconductivity · 2019
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCurrent (fluid)Materials scienceFinite element methodFault (geology)MechanicsWork (physics)High-temperature superconductivityFlow (mathematics)SuperconductivityComputer scienceElectrical engineeringMechanical engineeringPhysicsCondensed matter physicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

In this work, we use numerical simulations to compare the performance of different promising architectures of HTS tape for DC fault current limitation. A 1-D finite-element model which solves the heat equation through the thickness of a high-temperature superconducting (HTS) tape at the location of a hot spot was coupled with a simple electrical circuit model to perform the simulations. Using the normal zone propagation velocity (NZPV) obtained from a distinct 3-D finite-element electrothermal model, the quench dynamics in HTS tapes can be predicted, which allows estimating quite accurately the fault current level vs time. The calculations indicate that the insertion of a current flow diverter in the tape architecture allows decreasing the fault current level more rapidly and reducing the temperature elevation, thanks to the increased NZPV.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.029
GPT teacher head0.285
Teacher spread0.256 · 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
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

Citations6
Published2019
Admission routes1
Has abstractyes

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