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Record W3160019751 · doi:10.1109/tia.2021.3080663

Design and Implementation of a Novel Interior Permanent Magnet Bearingless Slice Motor

2021· article· en· W3160019751 on OpenAlexaff
Benjamin S. Weinreb, Minkyun Noh, Donald C. Fyler, David L. Trumper

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMagnetControl theory (sociology)StatorLevitationTorqueRotor (electric)Decoupling (probability)PhysicsTopology (electrical circuits)EngineeringComputer scienceMechanical engineeringElectrical engineeringControl engineering

Abstract

fetched live from OpenAlex

In this paper, we present a bearingless motor with a novel segmented dipole interior permanent magnet (IPM) slice rotor. The segmented dipole IPM rotor contains a unique pattern of interior permanent magnets arranged to generate a dipole air gap flux pattern. The magnets are encapsulated within an electrical steel rotor structure. The stator contains a three-phase, four-pole winding for suspension and a three-phase, two-pole winding for rotation. We present analyses of several candidate rotor designs. The analyses indicate that the segmented dipole IPM rotor achieves a reduced trade-off between force and torque capacity and relatively symmetric force dynamics as compared to prior art designs and alternate topologies. Symmetric and decoupled force dynamics allow a simple force decoupling algorithm to be used. We designed, constructed, and tested a prototype system. We experimentally demonstrate that the prototype system can achieve stable levitation and open-loop rotation.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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
GenreMethods

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

Citations26
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Industry ApplicationsSame topicMagnetic Bearings and Levitation DynamicsFrench-language works237,207