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Record W2013157899 · doi:10.1109/afrcon.2009.5308164

Mass-losses relationship in an optimized 8-pole radial AMB for Long Term Flywheel Energy Storage

2009· article· ca· W2013157899 on OpenAlexaff
L. Bakay, Maxime R. Dubois, P. Viarouge, Jean Ruel

Bibliographic record

Venuenot available
Typearticle
Languageca
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWindageFlywheelRotor (electric)Magnetic bearingControl theory (sociology)Finite element methodFlywheel energy storageMagnetic levitationWork (physics)Energy storageCounterweightMechanicsLevitationPower (physics)Eccentricity (behavior)Materials scienceEngineeringMagnetPhysicsStructural engineeringComputer scienceAutomotive engineeringMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents the effect of losses on a radial active magnetic bearings (AMB), used in the long term flywheel energy storage (LTFES). The study does not take into account the losses due to power electronics and windage losses (due to the friction between external rotor surface and the air). Therefore, we restricted the loss computation to those of radial AMB only. To simplify this work, we focused on the effect of external disturbance on unbalance force induced by rotor eccentricity for instance. The unbalance force has fixed the maximum radial load capacity of the rotor in order to design the radial AMB. 2-D finite element method (FEM) has been used to validate the theoretical model. We have optimized AMB system in order to search the best design which minimises overall losses (Copper and iron Losses) for different magnitudes of external force. Then, mass versus losses curve of AMB has been shown for the same angular velocity speed of rotor namely 9000 rpm.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.647
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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.

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

Citations8
Published2009
Admission routes1
Has abstractyes

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