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Record W2650045023 · doi:10.1115/1.4038061

Transient Heat Transfer in an Out-of-Pile SCWR Fuel Assembly Test at Near-Critical Pressure

2017· article· en· W2650045023 on OpenAlexaboutno aff
Thomas S. Schulenberg, Hongbo Li

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsnot available
Fundersnot available
KeywordsSupercritical fluidHeat transferBoilingMaterials scienceCoolantNucleate boilingThermodynamicsLeidenfrost effectCabin pressurizationThermal conductionCritical heat fluxMechanicsNuclear engineeringHeat transfer coefficientComposite materialEngineering

Abstract

fetched live from OpenAlex

While supercritical water is a perfect coolant with excellent heat transfer, a temporary decrease of the system pressure to subcritical conditions, either during intended transients or by accident, can easily cause a boiling crisis with significantly higher cladding temperatures of the fuel assemblies. These conditions have been tested in an out-of-pile experiment with a bundle of four heated rods in the supercritical water multipurpose loop (SWAMUP) facility coconstructed by CGNPC and SJTU in China. Some of the transient tests have been simulated at KIT with a one-dimensional (1D) matlab code, assuming quasi-steady-state flow conditions, but time dependent temperatures in the fuel rods. Heat transfer at supercritical and at near-critical conditions was modeled with a recent look-up table of Zahlan (2015, “Derivation of a Look-Up Table for Trans-Critical Heat Transfer in Water Cooled Tubes,” Ph.D. dissertation, University of Ottawa, Ottawa, ON, Canada.), and subcritical film boiling was modeled with the look-up table of Groeneveld et al. (2003, “A Look-Up Table for Fully Developed Film Boiling Heat Transfer,” Nucl. Eng. Des., 225(1), pp. 83–97.). Moreover, a conduction controlled rewetting process was included in the analyses, which is based on an analytical solution of Schulenberg and Raqué (2014, “Transient Heat Transfer During Depressurization From Supercritical Pressure,” Int. J. Heat Mass Transfer, 79(12), pp. 233–240.). The method could well reproduce the boiling crisis during depressurization from supercritical to subcritical pressure, including rewetting of the hot zone within some minutes, but the peak temperature was somewhat under-predicted. Tests with a lower heat flux, which did not cause such phenomena, could be predicted as well. In another test with increasing pressure, however, a boiling crisis was also observed at a heat flux, which was significantly lower than the critical heat flux (CHF) predicted by the CHF look-up table of Groeneveld et al. (2007, “The 2006 CHF Look-Up Table,” Nucl. Eng. Des., 237(15–17), pp. 1909–1922.). The paper is summarizing the physical models and the numerical approach. Comparison with experimental data is used to discuss the applicability of the method for the design of supercritical water-cooled reactors (SCWR).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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 designBench or experimental
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

Citations5
Published2017
Admission routes1
Has abstractyes

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