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Record W4411787206 · doi:10.3390/acoustics7030041

Acoustic Noise Characterization of a Switched Reluctance Motor Using Sound Power and Psychoacoustic Metric Measurements

2025· article· en· W4411787206 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAcoustics · 2025
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychoacousticsAcousticsSwitched reluctance motorSound (geography)Metric (unit)Noise (video)Computer sciencePhysicsEngineeringElectrical engineeringPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents an experimental acoustic noise characterization of a switched reluctance motor (SRM) designed for a wind turbine pitch angle control application. It details the fixture design for holding and positioning the sound intensity probes, along with the essential hardware setup for conducting acoustic noise experiments. Additionally, the software configuration is described to ensure compliance with specific measurement requirements. To study the effect of speed and load variations on the motor’s acoustic noise characteristics, tests are conducted at various operating points. The tests employ pulse-width modulation (PWM) current control, operating at a switching frequency of 12.5 kHz. Sound pressure and sound intensity are measured across different operating conditions to determine the sound power and psychoacoustic metrics. Furthermore, the effect of different factors on the motor’s sound power level, as well as on psychoacoustic metrics such as sharpness, loudness, and roughness, is analyzed and discussed.

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.

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 score0.950
Threshold uncertainty score0.758

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 integrity0.0000.000
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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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