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Record W1995305276 · doi:10.1115/pvp2003-2080

A New Tube/Support Impact Model for Heat Exchanger Tubes in Loose Supports

2003· article· en· W1995305276 on OpenAlexaff
Marwan Hassan, D. S. Weaver, M. A. Dokainish

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsMcMaster UniversityUniversity of New Brunswick
Fundersnot available
KeywordsHeat exchangerTube (container)RubbingMaterials scienceNonlinear systemStiffnessWork (physics)VibrationMechanicsMechanical engineeringStructural engineeringComputer scienceEngineeringComposite materialAcousticsPhysics

Abstract

fetched live from OpenAlex

Heat exchanger tubes are usually loosely supported at intermediate points by plates or flat bars. Flow-induced vibrations result in fretting wear tube damage due to impacting and rubbing of tubes against their supports. Prediction of tube response relies on modelling the nonlinear tube/support interaction. The evaluated response is used to predict the resultant wear damage using experimentally measured wear coefficients. An accurate of prediction of impact forces and the work rate is therefore paramount. The analytical models available assume tube/support contact occurs over a single point. In this paper, a computational algorithm is proposed to describe the tube/support impact considering a finite support width. The new model provides a means of representing tube/support contact as a combination of edge and segmental contact. The proposed model utilizes a distributed contact stiffness to describe the segmental contact. The formulation also incorporates a stick/slip friction model. The model developed is utilized to simulate the dynamics of loosely-supported tubes.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.023
GPT teacher head0.263
Teacher spread0.240 · 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 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

Citations0
Published2003
Admission routes1
Has abstractyes

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