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Record W1660311343 · doi:10.1109/elinsl.1992.246956

Application of dynamic mechanical analysis to endurance testing of generator stator bars

2003· article· en· W1660311343 on OpenAlexaff
J.M. Braun, G.C. Stone, H.G. Sedding

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsHydro One (Canada)
Fundersnot available
KeywordsTemperature cyclingDynamic mechanical analysisMaterials scienceStatorFlexural strengthStack (abstract data type)Delamination (geology)Generator (circuit theory)Dissipation factorComposite materialThermal analysisThermalMechanical engineeringStructural engineeringDielectricComputer scienceEngineering

Abstract

fetched live from OpenAlex

The thermal cycling test for generator coils and bars combines a modest increase in temperature cycling rates and cycle temperatures compared to normal operation. This test is performed on full size stator bars and coils and provides valuable data on the ability of the groundwall insulation to resist delamination and remain well bonded to the copper stack. To gain insight into the behavior of the resin component of the insulation system, dynamic mechanical analysis is proposed as a tool to aid in the development of thermally stable insulation systems for continuous high temperature operation and to support current efforts in determining the optimum parameters for thermal cycling tests. Flexural modulus and loss tangent measurements obtained by dynamic mechanical analysis represent a powerful and convenient way to ascertain mechanical properties and their evolution with temperature using microspecimens. Excellent correlation was demonstrated with flexural strength for homogeneous materials, over a broad temperature range.>

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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 designBench or experimental
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

Citations1
Published2003
Admission routes1
Has abstractyes

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