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Record W2099649087 · doi:10.1109/temc.2003.811304

BLDC motor and drive conducted RFI simulation for automotive applications

2003· article· en· W2099649087 on OpenAlexaff
J.E. Makaran, Joe LoVetri

Bibliographic record

VenueIEEE Transactions on Electromagnetic Compatibility · 2003
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsUniversity of ManitobaSiemens (Canada)
Fundersnot available
KeywordsElectromagnetic interferenceAutomotive industryRange (aeronautics)Noise (video)CapacitorEngineeringElectromagnetic compatibilityAutomotive engineeringRadio frequencyElectronic engineeringComputer scienceElectrical engineeringVoltageAerospace engineering

Abstract

fetched live from OpenAlex

In considering automotive conducted radio-frequency-interference (RFI) specifications applicable to motors and their associated drives, simulation of conducted RF emissions in the range from 150 kHz to 30 MHz is an area of interest from the product design perspective for several reasons. Traditionally, suppression of conducted noise in this frequency range of interest has been achieved through the use of bulk suppression elements such as capacitors and inductors. These elements consume valuable space within the motor, as well as add cost. The selection of bulk noise suppression elements, has, in the past, been predominately made through trial and error "brute force" methods. A method is presented whereby conducted RFI emissions can be simulated through the use of a high-fidelity virtual motor and drive model, as well as a virtual spectrum analyzer. Experimental validation of the model shows that accurate predictions can be made in the low-frequency range, below 10 MHz. Suggestions are made on how to improve the model at higher frequencies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
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)0.0000.000

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 head0.012
GPT teacher head0.242
Teacher spread0.229 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

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