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Record W2615595751 · doi:10.1049/iet-epa.2017.0072

Optimisation‐based procedure for characterising switched reluctance motors

2017· article· en· W2615595751 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.

Bibliographic record

VenueIET Electric Power Applications · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSwitched reluctance motorControl theory (sociology)Reluctance motorControl engineeringComputer scienceAutomotive engineeringEngineeringMechanical engineeringRotor (electric)Artificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

This study introduces an optimisation‐based procedure for characterising switched reluctance machine (SRM) performance and studies the optimisation to determine the conduction angles in SRM drives. The objectives employed in the optimisation cases are maximising average output torque, maximising the ratio of average torque over root mean square (RMS) value of phase current, and minimising RMS value of net torque ripple. Combinations of these objectives are used in four different cases, which are formulated either as single‐ or multi‐objective problems. These cases are then compared in terms of output torque, torque ripple, and efficiency. One method of the four is selected and the performance of the motor over the entire operating range is characterised based on optimised turn‐on and turn‐off angles. Experimental results are used to verify the motor performance obtained from the optimisations for selected operating points.

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 score0.950
Threshold uncertainty score0.827

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 studies0.0010.000
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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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