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Record W2001654905 · doi:10.1115/pvp2007-26082

A Simple Energy Approach to Assess Vibration and Fretting-Wear Damage in Process Equipment

2007· article· en· W2001654905 on OpenAlexaff
M. J. Pettigrew, M. Yetisir, Nigel J. Fisher, Colette E. Taylor, Bruce Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsAtomic Energy (Canada)Polytechnique Montréal
Fundersnot available
KeywordsFrettingPipingVibrationHeat exchangerStructural engineeringMaterials scienceWork (physics)Vortex-induced vibrationFretting wearFinite element methodEnergy (signal processing)Mechanical engineeringMechanicsEngineeringAcoustics

Abstract

fetched live from OpenAlex

Excessive flow-induced vibration causing fretting-wear damage can seriously affect the performance of process equipment such as heat exchangers, condensers, nuclear steam generators, nuclear fuels, reactor internals, and piping systems. Fretting-wear damage generally takes place between a vibrating structure and its supports. It can be predicted with a fretting-wear coefficient obtained experimentally and a parameter called work-rate that formulates the dynamic interaction between structure and support. The work-rate is essentially the rate of mechanical energy dissipated at the support. On the other hand, the total available mechanical vibration energy in a structure is related to its mass, vibration frequency, mode shape, damping, and vibration amplitude. This leads to the development of a simplified formulation based on energy considerations to relate the vibration response of a structure to fretting-wear damage at its supports. The basic energy equations and the formulation of a simplified energy relationship to predict fretting-wear damage are outlined in this paper. The relationship is verified against experimental data for a multi-span heat exchanger tube. The energy approach is also compared to time domain calculations performed with a non-linear finite element code. The results indicate that the simple energy approach may be very useful to estimate fretting-wear damage in practical situations. Finally, the application of the method is illustrated for a typical heat exchanger tube and for nuclear fuels.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.274

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 head0.024
GPT teacher head0.258
Teacher spread0.234 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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