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Record W2326499892 · doi:10.1109/tec.2016.2539959

A New Stray-Load Loss Formula for Small and Medium-Sized Induction Motors

2016· article· en· W2326499892 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

VenueIEEE Transactions on Energy Conversion · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsHydro-QuébecConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsInduction motorHorsepowerRange (aeronautics)EngineeringElectronic engineeringComputer scienceAutomotive engineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

This paper proposes a new stray-load loss (SLL) formula for small and medium induction motors (IMs) based on tests data of a 182, 60 Hz IMs in the range of 1-500 hp (0.75-375 kW). They are all tested in accordance with IEEE Std 112-Method B. The proposed formula is validated by recalculating the efficiency of the same number of motors by using the proposed formula, as well as the IEEE Std 112 and the IEC 60034-2-1 standards. Another validation was done on testing 17 additional IMs that are independent of the 182-motor data. In both validations, the new formula demonstrates better accuracy. This formula shows the potential to replace the existing SLL estimation formula for this horsepower range.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.532

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 head0.010
GPT teacher head0.191
Teacher spread0.180 · 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