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Record W4313235932 · doi:10.14447/jnmes.v25i4.a08

Implantation of Coated Superconducting Materials in the Synchronous Machine for Superconducting Energy Storage

2022· article· en· W4313235932 on OpenAlexvenueno aff
Salah Belkhiri, Zine Ghemari, Mohamed Lotfi Khene, Fethi Ben Mebarek, Salah Saad

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsnot available
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsSuperconductivityDiamagnetismFinite element methodSuperconducting magnetic energy storageElectromagnetic fieldMagnetic fieldPhysicsComputer scienceMaterials scienceSuperconducting magnetMechanicsMechanical engineeringComputational physicsCondensed matter physicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Numerical tools appear to be essential for modeling and designing devices based on superconducting materials. In this article different simulation results are presented, using a computer code based on the finite element method adopted for the resolution of the electromagnetic equations, in the case of an axisymmetric two-dimensional problem, with this code we study the variations of the different electromagnetic quantities. The second generation superconductor has been modeled as an interesting diamagnetic material as inductive pulse sources. The performance in magnetic field resistance, energy storage and thermal stability of the ribbon, known as YBCO, makes it possible to broaden its field of application. Two categories of machines have been proposed and analyzed, the first is classic and the second uses a superconducting ribbon. In addition, a comparative study between the two proposed models is carried out and the results are analyzed and discussed.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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