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Record W4246414395 · doi:10.1115/1.4047496

A Design Guideline for Random Excitation Forces Due to Two-Phase Cross Flow in Tube Bundles

2020· article· en· W4246414395 on OpenAlexaff
Colette E. Taylor, M. J. Pettigrew

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicNuclear Engineering Thermal-Hydraulics
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsFlow (mathematics)MechanicsDimensionless quantityWork (physics)Tube (container)AmplitudeMathematicsPhysicsEngineeringMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

Abstract This paper re-examines the available experimental data to investigate the random excitation forces that affect tube bundles exposed to two-phase cross flow. Much of the experimental data generated over the past four decades have been gathered in an attempt to understand the parametric dependence of the random two-phase forces. The data include air–water, steam–water and various Freons used in a variety of test sections with either strain gages to measure the tube amplitude or force transducers to measure the reaction forces. A review of previous work in this area finds that some authors claim a strong flow regime dependence while others suggest that this dependence is weak. This work takes a detailed look at this discrepancy and finds that a single design guideline does not adequately bound all flow regimes. As a result, two dimensionless upper bounds are proposed.

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)0.003
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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