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Record W2034761972 · doi:10.1115/1.1576427

Test Results for PTFE-Faced Thrust Pads, With Direct Comparison Against Babbitt-Faced Pads and Correlation With Analysis

2003· article· en· W2034761972 on OpenAlexaff
C. M. McC. Ettles, R T Knox, J. H. Ferguson, Dietrich Hörner

Bibliographic record

VenueJournal of Tribology · 2003
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsBabbittCreepBearing (navigation)Lift (data mining)Thrust bearingThrustMaterials sciencePower (physics)Mechanical engineeringForensic engineeringStructural engineeringComposite materialComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

The use of PTFE-faced pads in large vertical axis hydro-generators was pioneered in Russia in the 1970s, prompted by a series of failures of conventional babbitt-faced bearings. Some advantages claimed include higher specific loading, lower power loss and the omission of oil-lift facilities. There is strong interest in the Industry concerning this material, but limited data are available on actual performance. Some results from extensive testing of PTFE-faced pads are given, for two sizes of pad. These are compared directly size-for-size with results for babbitt bearings of nominally the same area. The power losses for the two types of bearing were found to be almost identical. Some of the effects observed during testing are described and discussed, including the effect of creep. The test results are compared with predictions using the GENMAT analysis software. A method of allowing for creep in numerical modeling is discussed.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations49
Published2003
Admission routes1
Has abstractyes

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