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Record W4243600869 · doi:10.1109/pcicon.2001.960579

Instrumentation, testing and analysis of electric machine rotor steady-state heating

2002· article· en· W4243600869 on OpenAlexaff
J.H. Dymond, R. Ong, N. Stranges

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsInstrumentation (computer programming)Rotor (electric)Steady state (chemistry)Electric heatingComputer scienceMechanical engineeringNuclear engineeringElectrical engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

It is not a simple task to measure temperatures on the rotors or rotor components of large induction and synchronous machines during steady state operation. The single greatest obstacle to obtaining this data is related to getting the data from the rotating element. Other problems are associated with instrumentation noise, sensor type and attachment. This paper describes a number of techniques that have been used by the authors and their associates over the last 30 years. Test and calculated temperatures are presented for one machine. Also, test data from a number of machines is presented that show for this particular population of enclosures and ventilation schemes the steady state temperatures at the design load can range from the value of the stator rise to greater than 70/spl deg/C higher.

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: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.014
GPT teacher head0.207
Teacher spread0.193 · 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

Citations10
Published2002
Admission routes1
Has abstractyes

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