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Record W2146819331 · doi:10.1109/20.877699

3D thermal stress analysis of the rotor of an induction motor

2000· article· en· W2146819331 on OpenAlexaff
Jungil Lee, Dongjin Bae, Jung-Lock Kwon, Keun-Woong Kim, Jong-Hak Youn, Song-Yop Hahn, Hyun-Kyo Jung, Yangsoo Lee, Yuangjiang Liu

Bibliographic record

VenueIEEE Transactions on Magnetics · 2000
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of British ColumbiaTRIUMF
Fundersnot available
KeywordsFinite element methodInduction motorRotor (electric)ThermalStress (linguistics)Thermal analysisMaterials scienceMechanicsSteady state (chemistry)Field (mathematics)Mechanical engineeringStructural engineeringPhysicsElectrical engineeringEngineeringThermodynamicsMathematics

Abstract

fetched live from OpenAlex

This paper analyzes the temperature distribution of an induction motor in steady state using a hybrid thermal network method-finite element method (TNM-FEM). 3 dimensional FEM has been used to analyze the thermal stresses on the rotor of an induction motor. The mechanical deformations and thermal stress distributions are obtained. The numerical analyzes of the thermal field are verified by experimental results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.197
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2000
Admission routes1
Has abstractyes

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