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Record W2610833597 · doi:10.1049/iet-epa.2016.0831

Hybrid analytical model of switched reluctance machine for real‐time hardware‐in‐the‐loop simulation

2017· article· en· W2610833597 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 institutionsUniversity of Alberta
Fundersnot available
KeywordsSwitched reluctance motorHardware-in-the-loop simulationLoop (graph theory)Computer scienceControl engineeringControl theory (sociology)EngineeringElectrical engineeringControl (management)Artificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Applications of switched reluctance machine (SRM) are increasing in the industry due to their many desirable features. This study proposes a hybrid analytical model (HAM) of the SRM for the hardware‐in‐the‐loop (HIL) simulation. To obtain satisfactory accuracy, the phase flux linkage is solved by the magnetic equivalent circuit (MEC) method when the stator and rotor poles overlap, and by the space harmonic method (SHM) when the poles do not overlap. The backward Euler and Newton–Raphson methods are used to calculate the exciting current, while the Gaussian quadrature is used to compute the electromagnetic torque in the HIL simulation. The digital hardware implementation of computation components are developed on the field‐programmable gate array by exploiting the parallel hardware architecture and fully pipelined arithmetic processing. To highlight the performance of the HAM, the captured real‐time results are compared with the off‐line transient solution obtained through the co‐simulation of Ansys Maxwell ® , Ansys Simplorer ® , and Simulink ® , which model the SRM, drive circuit, and control system, respectively.

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 score0.967
Threshold uncertainty score0.558

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 science0.0010.000
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 head0.016
GPT teacher head0.267
Teacher spread0.252 · 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