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Record W2194603331 · doi:10.1115/1.4032198

An Experimental Model Study of Steam Generator Tube Loading During a Sudden Depressurization

2015· article· en· W2194603331 on OpenAlexaff
Ouajih Hamouda, David S. Weaver, Jovica Riznic

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicNuclear Engineering Thermal-Hydraulics
Canadian institutionsCanadian Nuclear Safety CommissionMcMaster University
FundersFramatome
KeywordsBundleBoiler (water heating)MechanicsTube (container)Transient (computer programming)Cabin pressurizationPressure dropMaterials scienceFlow (mathematics)Structural engineeringEngineeringThermodynamicsComposite materialPhysicsComputer science

Abstract

fetched live from OpenAlex

This paper presents the results of an experimental model study of the transient loading of steam generator tubes during a postulated main steam line break (MSLB) accident in a nuclear power plant. The problem involves complex transient two-phase flow dynamics and fluid-structural loading processes. A better understanding of this phenomenon will permit the development of improved design tools to ensure steam generator tube integrity. The pressure and temperature were measured upstream and downstream of a sectional model of a tube bundle in cross-flow, and the transient tube loads were directly measured using dynamic piezoelectric load cells. High-speed videos were taken to observe and better understand the flow phenomena causing the tube loading. The working fluid was R-134a and the tube bundle was a normal triangular array with a pitch ratio of 1.36. The flow through the bundle was choked for the majority of the transient. The transient tube loading is explained in terms of the associated fluid mechanics. An empirical model is developed that enables the prediction of the maximum tube loads once the pressure drop is known.

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)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.239
Teacher spread0.226 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pressure Vessel TechnologySame topicNuclear Engineering Thermal-HydraulicsFrench-language works237,207