MétaCan
Menu
Back to cohort
Record W2149079127 · doi:10.1109/iemdc.2009.5075404

Simplified design model for fast analysis of large synchronous generators with magnetic saturation

2009· article· en· W2149079127 on OpenAlexaff
J. Cros, M. Taghizadeh, Jose R. Figueroa, L. Radaorozandry, P. Viarouge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAlternatorStatorMagnetic reluctanceElectromagnetic coilEquivalent circuitControl theory (sociology)Magnetic circuitMagnetic fluxRotor (electric)Permanent magnet synchronous generatorComputer scienceSynchronous motorEngineeringMagnetPower (physics)Mechanical engineeringElectrical engineeringVoltagePhysicsMagnetic field

Abstract

fetched live from OpenAlex

This paper presents simplified alternator design tools which could evaluate the performances of a large machine in function of its geometrical dimensions and winding parameters. This method is well adapted for fast comparisons of different alternator structures and a first optimization of the alternator characteristics. It is based on two kinds of analytical models for the estimation of the equivalent electrical circuit parameters. These models are using a small non-linear reluctance network of one pole domain and a solution of the linear Maxwell equations in the air-gap of a simplified structure of entire machine to calculate inductances and no-load flux by taking account winding characteristics. We simulate two classical machine tests (stator short-circuit and no-load operation) for different rotor excitation currents for the identification of a saturated circuit equivalent parameters. We present two examples using the specifications and the characteristics of existing hydraulic alternators. We make some comparisons with FE method and validate the performances of the original machines as found with the analytical saturated model with those defined by the machine specifications.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.211
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

Citations4
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicElectric Motor Design and AnalysisFrench-language works237,207