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ICONE19-44082 EFFECT OF HEAT TRANSFER COEFFICIENT ON SHEATH AND FUEL CENTRELINE TEMPERATURES IN SCWRS

2011· article· en· W2147630208 on OpenAlexafffund
Lisa Grande, Wargha Peiman, Bryan Villamere, Adrianexy Rodriguez-Prado, Sally Mikhael, Leyland Allison, Igor Pioro

Bibliographic record

VenueThe Proceedings of the International Conference on Nuclear Engineering (ICONE) · 2011
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsCoolantHeat transferSupercritical fluidHeat transfer coefficientMaterials scienceNuclear engineeringHeat fluxThermodynamicsMechanicsPhysicsEngineering

Abstract

fetched live from OpenAlex

SuperCritical Water-cooled nuclear Reactors (SCWRs) utilize light water above the pseudocritical point as a reactor coolant. This Generation IV reactor concept is currently in its preliminary design phase. This paper discusses the variables that influence heat transfer from the fuel. The coolant Heat Transfer Coefficient (HTC), Axial Heat Flux Profile (AHFP), sheath (clad) geometry and fuel thermal conductivity all impact the sheath and fuel centreline temperatures. The presented analysis utilizes the most recent HTC correlation developed for supercritical water, the Mokry et al. correlation (2009). The proposed sheath geometry is based on smaller diameter fuel elements than that of the current design to accommodate more fuel rods. Both uniform and cosine AHFPs, at average channel power, are applied. The results presented describe a sensitivity analysis of the effect of incrementing the HTC on sheath and fuel centreline temperatures. With a uniform AHFP and increasing HTC increments from 50-200% HTC the average temperature difference compared to 100% HTC are decreases from 13 to -8%. With a cosine AHFP and HTC incremented from 50-200% HTC the average temperature difference compared to 100% HTC have the range of 20 to -10%.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.287
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.200
Teacher spread0.184 · 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 teacher head, 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

Citations0
Published2011
Admission routes2
Has abstractyes

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