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Record W2122851901 · doi:10.3390/su7033441

Sustainability Aspects of Energy Conversion in Modern High-Speed Trains with Traction Induction Motors

2015· article· en· W2122851901 on OpenAlexaff
Marc A. Rosen, Doru A. Nicola, Cornelia Aida Bulucea, Daniel Cristian Cismaru

Bibliographic record

VenueSustainability · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTraction motorAutomotive engineeringTraction (geology)Induction motorEngineeringTrainElectric vehicleElectric motorSustainabilityConvertersRegenerative brakeVoltageElectrical engineeringMechanical engineeringBrake

Abstract

fetched live from OpenAlex

Some aspects are illustrated of energy conversion processes during the operation of electric railway vehicles with traction induction motors, in order to support transport systems’ sustainability. Increasing efforts are being expended to enhance the sustainability of transportation technologies and systems. Since electric drive systems are used with variable voltage variable frequency (VVVF) inverters and traction induction motors, these machines with appropriate controls can realize both traction and electric braking regimes for electric traction vehicles. In line with this idea, this paper addresses the operation sustainability of electric railway vehicles highlighting the chain of interactions among the main electric equipment on an electrically driven railway system supplied from an a.c. contact line: The contact line-side converter, the machine-side converter and the traction induction motor. The paper supports the findings that electric traction drive systems using induction motors fed by network-side converters and VVVF inverters enhance the sustainable operation of railway trains.

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)0.001
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: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.211
Teacher spread0.203 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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