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Record W1560254796 · doi:10.1063/1.2900392

MODELING OF HTS APPLICATIONS USING EMTP WITH FLUX-PINNING SCALING MODELS FOR PRACTICAL HTS SUPERCONDUCTORS

2008· article· en· W1560254796 on OpenAlexaff
J.R. Cave, Vijay K. Sood, U. Balachandran, Kathleen Amm, David Evans, E. Gregory, Peter Lee, Mike Osofsky, Sastry Pamidi, Chan Park, Judy I. Wu

Bibliographic record

VenueAIP conference proceedings · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsEmtpTransient (computer programming)LimiterScalingFault (geology)Materials scienceElectric power systemMagnetic fluxFault current limiterElectronic engineeringPower (physics)Computer scienceElectrical engineeringMagnetic fieldPhysicsEngineering

Abstract

fetched live from OpenAlex

The EMTP software (Electromagnetic Transient Program) for modeling the behavior of electric power systems is commonly used to obtain the transient response of system disturbances, for example caused by fault currents. We have developed specific modules describing practical HTS materials that can be used to describe new architectures involving superconducting power devices, for example fault current limiters. These modules include flux-pinning scaling models that vary smoothly in their parameterization of the J-T-B-E (current density, temperature, magnetic field and electric field) surfaces. In addition, the non-linear low temperature materials properties, such as specific heat, are included for a more accurate description of device behavior. The advantage of this approach is that the complex and non-linear flux-pinning and thermal characteristics of HTS devices can be integrated into power network models. The modeling is presented, with an emphasis on fault current limiters where three domains of operating characteristics are identified.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.992

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 communication0.0000.003
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 head0.095
GPT teacher head0.296
Teacher spread0.201 · 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 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

Citations3
Published2008
Admission routes1
Has abstractyes

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