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Control of a Linear Switched Reluctance Motor in Electric Train Application

2023· article· en· W4387005516 on OpenAlexaff
Siamak Masoudi, Atif Iqbal, Nasser Al‐Emadi, Hasan Mehrjerdi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsSwitched reluctance motorElectric motorLinear motionLinear motorReluctance motorControl theory (sociology)Work (physics)ConvertersAutomotive engineeringComputer scienceRotation around a fixed axisMotion controlTrainReduction (mathematics)EngineeringControl (management)VoltageElectrical engineeringMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Linear electrical motors are good candidates for the industries with linear motion. The use of rotational electrical motors in linear movements, require additional mechanical converters, which in addition to reducing system efficiency, also increase maintenance costs. Linear electric motors are similar to rotary motors in terms of structure and performance, except that instead of rotary motion, they directly create linear motion. Therefore, they can be very effective in certain applications such as electric trains. By eliminating the mechanical converter between the electric motor and the wheels, efficiency is increased, while noise pollution is significantly reduced. Moreover, due to the reduction of heat losses caused by friction, the device is able to move at high speeds. In this work, application and control of a linear switched reluctance motor in railway system has been proposed which is low cost comparing with other linear motors. Simulation results confirm that the proposed motor can be a suitable candidate for the application.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.007
GPT teacher head0.207
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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