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Record W2126497630 · doi:10.1109/tmag.2003.812707

Time-stepped eddy-current analysis of induction machines with transmission line modeling and domain decomposition

2003· article· en· W2126497630 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

VenueIEEE Transactions on Magnetics · 2003
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEddy currentSolverComputer scienceDomain decomposition methodsTransmission lineNonlinear systemTime domainCurrent (fluid)Control theory (sociology)PhysicsFinite element method

Abstract

fetched live from OpenAlex

This paper presents an approach to using domain decomposition (DD) for time-stepped eddy-current analysis of induction machines. The approach uses the transmission line modeling (TLM) method to linearize the nonlinear field equations, producing a stiffness matrix that is more amenable to decomposition than that obtained by using a Newton-Raphson approach. Using a direct solver, the paper compares the approaches and shows that the proposed approach can offer simulation times that are significantly shorter than those for the traditional Newton-Raphson approach. The paper describes the steps necessary to implement the TLM-DD approach for time-stepped eddy-current analysis of induction machines. Test results for steady-state and dynamic conditions are provided.

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 score0.596
Threshold uncertainty score0.589

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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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