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A Study of the Relationship between Acoustic Noise and Torque Pulsation in Permanent Magnet Synchronous Motors

2022· article· en· W4309227419 on OpenAlexaff
Issah Ibrahim, David A. Lowther

Bibliographic record

Venue2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC) · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsTorque rippleDirect torque controlTorqueNoise (video)Electric motorControl theory (sociology)MagnetComputer scienceStall torqueSynchronous motorTorque densityVibrationTorque motorDamping torquePhysicsAcousticsEngineeringElectrical engineeringVoltageInduction motorArtificial intelligence

Abstract

fetched live from OpenAlex

Acoustic noise and torque pulsation, also known as torque ripple, are some of the undesirable performance indices in the design of electric motors. In fact, knowing the relationship between the two quantities can be very crucial, especially in the optimization cycle of the electric machine. For instance, where an increase in torque ripple is known to exacerbate the noise and vibrations, the two quantities could be considered as non-conflicting objectives. What this means is that minimizing one quantity could result in the optimization of the other without necessarily treating the two as mutually exclusive objectives. This paper, therefore, attempts to explore the correlation, if any exists, between torque ripple and the acoustic noise performance of the electric motor. The procedure involves exploring the design space of a 10 pole 12 slot surface-mounted permanent magnet synchronous motor to compute the torque ripple and the corresponding acoustic noise related to thousands of different motor configurations. Then, a statistical analysis is performed to establish the relationship between the two quantities.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.239
Teacher spread0.218 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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