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Record W4241711708 · doi:10.1109/pcicon.2002.1044994

How design influences the temperature rise of motors on inverter drives

2003· article· en· W4241711708 on OpenAlexaff
N. Stranges, J.H. Dymond

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsDeratingHarmonicsInverterAutomotive engineeringPower (physics)EngineeringHarmonicElectric motorInduction motorComputer scienceElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Drives and electric motors are sometimes paired in order to improve efficiency and process control or to eliminate gears and reduce maintenance costs. Sometimes the addition of the drive to an existing installation has not been the success that was originally envisioned and substantial derating has occurred or the original motor or drive replaced. One industry standard for product certification requires that there be a 30/spl deg/C margin in the temperature rise if a motor intended for use on a drive is acceptance tested on sinusoidal power. The intent of this temperature margin is to allow for the additional heating losses due to inverter harmonics. Machine design, especially the ventilation, has a significant impact on the difference in temperature rise due to the increased harmonic losses. This paper discusses a number of different ventilation methods present in motors. The results of tests done on machines using the same load on both sinusoidal power and inverters are also presented.

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.185
Teacher spread0.175 · 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 designBench or experimental
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
Published2003
Admission routes1
Has abstractyes

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