MétaCan
Menu
Back to cohort
Record W2001524328 · doi:10.1109/iemdc.2013.6556289

Design of slotted permanent magnet linear synchronous motor for improved thrust density

2013· article· en· W2001524328 on OpenAlexaff
Nariman Roshandel Tavana, Venkata Dinavahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThrustMagnetLinear motorSynchronous motorPermanent magnet synchronous generatorPermanent magnet synchronous motorComputer scienceAutomotive engineeringPhysicsElectrical engineeringControl theory (sociology)Aerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Since a linear motor with high thrust density and low thrust ripple is desired for many industrial applications, in this paper, a design methodology is proposed to achieve these objectives. A slotted permanent magnet linear synchronous motor (SPMLSM) which inherently offers the highest force density among various types of linear motors is selected for analysis and optimization procedure. An analytical model is derived for the SPMLSM by solving Maxwell's equations for calculating the magnetic field and force. Motor dimensions are then optimized using the analytical model and genetic algorithm, where the increase of force density and the reduction of thrust ripple are considered as the optimization target. In this study, to reach a higher optimal point in the multidimensional search space of the machine design, cogging force caused by slot effect is initially neglected in the model. Subsequently, pole-shifting is applied to the optimal model to eliminate cogging force and satisfy design objectives. Finally, the effectiveness of the proposed technique to enhance motor performance is investigated by a time-stepping transient finite-element method. The results show an improvement in the performance of optimized motor.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.728

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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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
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

Citations10
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicElectric Motor Design and AnalysisFrench-language works237,207