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Record W2767121221 · doi:10.1115/pvp2017-65272

Fretting-Wear Damage due to Vibration in Nuclear and Process Equipment

2017· article· en· W2767121221 on OpenAlexaff
M. J. Pettigrew, M. Yetisir, Nigel J. Fisher, Bruce Smith, Colette E. Taylor, Victor P. Janzen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsCanadian Nuclear LaboratoriesPolytechnique Montréal
Fundersnot available
KeywordsFrettingPipingFretting wearMaterials scienceWork (physics)VibrationTube (container)Heat exchangerStructural engineeringMechanical engineeringForensic engineeringComposite materialEngineeringAcoustics

Abstract

fetched live from OpenAlex

The problem of fretting-wear damage between a vibrating structure and its supports is discussed in this paper. Typical components of concern are piping systems and pipe-supports, multispan heat exchanger tubes and tube supports, and nuclear fuel bundles and fuel channels. Fretting-wear damage is related to the dynamic interaction between a structure and its supports. This interaction is conveniently formulated in terms of a parameter called “work rate” to predict fretting-wear damage. Work rate is simply the integral of contact force over sliding distance per unit time. Fretting-wear damage may be investigated from an energy point of view. It is essentially the mechanical energy or power dissipated through contact forces and sliding that causes fretting-wear damage. Development of a simple formulation that relates tube vibration response and fretting-wear damage is reviewed in this paper. Some new practical examples and simple calculations are 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 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 score0.836
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

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

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 head0.245
Teacher spread0.233 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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