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Record W9112267 · doi:10.13182/nt12-a13330

Numerical Study on the Turbulent Mixing Coefficient for Supercritical Fluids in Subchannels

2012· article· en· W9112267 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNuclear Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsWestern University
Fundersnot available
KeywordsSupercritical fluidTurbulenceReynolds numberMechanicsMixing (physics)Materials scienceThermal hydraulicsBundleHydraulic diameterHeat transfer coefficientComputational fluid dynamicsSupercritical flowHeat transferThermodynamicsPhysicsComposite material

Abstract

fetched live from OpenAlex

In this study, computational fluid dynamics simulations are carried out to predict the thermal-hydraulic behavior of supercritical fluids in the subchannel of supercritical water-cooled reactor (SCWR) fuel channels. The thermal-hydraulic behavior of supercritical water in triangular array and square array fuel rod bundles is studied numerically. The effects of various parameters including the pitch-to-diameter ratio and Reynolds number on the flow and the heat transfer characteristics are investigated. It is found that the turbulent mixing coefficient of supercritical water in subchannels is strongly dependent on the fluid bulk temperature and pitch-to-diameter ratio in the vicinity of the pseudo-critical point. To have a higher overall turbulent mixing coefficient, a pitch-to-diameter ratio less than 1.2 is recommended for the design of SCWR. The turbulent mixing coefficient correlation for the triangular array rod bundle is developed in this study based on the numerical results. However, the correlation for the mixing coefficient for the square array rod bundle cannot be expressed as a general correlation.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.532

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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