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Test of a Flux Modulation Superconducting Machine for Aircraft

2020· article· en· W3046577687 on OpenAlexaff
Alexandre Colle, Thierry Lubin, Sabrina Ayat, Olivier Gosselin, Jean Lévêque

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsSuperconductivityPropulsionElectrically powered spacecraft propulsionSuperconducting electric machineInductorMechanical engineeringTopology (electrical circuits)Automotive engineeringElectrical engineeringComputer scienceAerospace engineeringEngineeringMagnetSuperconducting magnetPhysicsSuperconducting magnetic energy storageCondensed matter physicsVoltage

Abstract

fetched live from OpenAlex

Abstract The increasing of drives towards More Electric Aircraft (MEA) or the development of electric propulsion aircraft calls for MW-class electrical machines with more compact and power dense designs. One way is to explore the use of superconducting (SC) materials to create a high magnetic field in order to reduce the mass of ferromagnetic components. This paper presents the design and the test of a brushless axial flux superconducting machine. The brushless topology satisfies the aeronautics industry requirements in terms of maintenance, while the axial configuration ensures an optimal use of the anisotropic HTS tapes. The machine is classed as partially superconducting, only the inductor is made with superconducting materials. A 50kW prototype is manufactured with a minimal mass objective. The prototype constitutes a first step to a scale-up MW-class machine design.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.246
Teacher spread0.199 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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