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Record W1967144259 · doi:10.1109/tie.2015.2392716

Loss and Efficiency Analysis of Switched Reluctance Machines Using a New Calculation Method

2015· article· en· W1967144259 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 Industrial Electronics · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsSwitched reluctance motorEddy currentMagnetic reluctanceControl theory (sociology)Finite element methodTraction (geology)Magnetic fluxMagnetic circuitComputer scienceDiscretizationEngineeringMagnetMagnetic fieldMathematicsRotor (electric)Electrical engineeringMechanical engineeringPhysicsMathematical analysisStructural engineering

Abstract

fetched live from OpenAlex

In this paper, a fast and accurate analytical method is proposed to analyze the loss and efficiency of switched reluctance machines (SRMs) under various operating conditions. The analyses are applied on a four-phase 16/12 SRM with high power density and wide speed range, which was designed for traction application. A direct method is proposed to calculate hysteresis and eddy current loss without empirical equations. The machine is discretized into a limited number of elements according to the magnetic flux density distribution using an analytical magnetic circuit method. Variable loss coefficients are used for each element to improve the accuracy. The developed method has been validated with simulation and experimental results.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.044
GPT teacher head0.284
Teacher spread0.240 · 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