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Record W4406354650 · doi:10.17743/jaes.2022.0188

Modeling the Effect of Eddy Currents on the Inductance of Loudspeaker Motors Using the Method of Lines

2025· article· en· W4406354650 on OpenAlexaff
Oliver Munroe, Antonín Novák, Laurent Simon, Daniel Massicotte

Bibliographic record

VenueJournal of the Audio Engineering Society · 2025
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsInductanceLoudspeakerEddy currentElectromagnetic coilDiscretizationBandwidth (computing)Finite element methodAcousticsSkin effectElectronic engineeringComputer sciencePhysicsEngineeringElectrical engineeringMathematical analysisMathematicsTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

This paper presents a way of modeling the effect of eddy currents on the inductance of a loudspeaker coil over a wide frequency bandwidth without the use of fractional derivatives or Foster/Cauer networks. The model presented is a physics-based model, which is derived from Maxwell’s equations and discretized in space. The model is compared with finite element analysis simulations and measurement data. The results show an excellent agreement with the reference data over the audio bandwidth and for different coil positions. Using eight parameters, of which four require fitting and only two are dependent on the coil position, the model is simple to fit to reference data and may be vectorized for real-time applications.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.295
Teacher spread0.276 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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