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Model for Choking of Subcooled Flashing Flow Through a Steam Generator Tube Crack

2012· article· en· W2015765588 on OpenAlexaff
Brian Wolf, Shripad T. Revankar, Jovica Riznic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsCanadian Nuclear Safety Commission
Fundersnot available
KeywordsFlashingChokingSubcoolingMechanicsCompressed fluidMaterials sciencePressure dropTwo-phase flowThermodynamicsFlow (mathematics)BoilingPhysicsMedicine

Abstract

fetched live from OpenAlex

Recently there is some database available on choking flow through cracks relevant to steam generator (SG) tubes to model the critical flow. A one dimensional mechanistic model was developed to model two-phase choking flow through slits from conservation principles. The model takes into account channel entrance loss as well as frictional pressure drop for single-phase subcooled liquid. Flashing criteria are defined and temperature and pressure of the fluid are assumed to follow the saturation curve. The two-phase mixture was treated as a quasi-fluid with mixture properties and both homogenous equilibrium (HE) and homogeneous non-equilibrium models were considered. The models were compared with the choking flow rates for various experimental conditions for subcooled flashing flow through narrow slits with L/D varying from small values ( 5) to large values (100). Results are presented on the effects of thermal non-equilibrium on the choking flow for small L/D channels. A comparison of model results to experimental data shows that the HE based models grossly under predict choking flow rates in such geometries. As subcooling increases, and channel length decreases the non-equilibrium effects play a greater role in the choking phenomenon, therefore the difference in model predictions and experimental results increases for HE case.

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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.030
GPT teacher head0.260
Teacher spread0.230 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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