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Record W2079646457 · doi:10.1115/icone21-16442

Heat Transfer to Water at Supercritical Parameters in Vertical Tubes, Annular Channels, 3- and 7-Rod Bundles

2013· article· en· W2079646457 on OpenAlexaff
V. G. Razumovskiy, E. M. Mayevskiy, A. E. Koloskov, Eugene Pis’mennyi, Igor Pioro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSupercritical fluidCoolantHeat transferMaterials scienceMass fluxInletMechanicsHeat fluxMass flow rateThermodynamicsMass flowThermal hydraulicsVolumetric flow rateFlow (mathematics)ThermalMass transferMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

The data on deteriorated transfer to supercritical water in vertical tubes and channels simulating coolant flow in fuel assemblies obtained at the same experimental setup during more than dozen of years are considered and compared with some known results of the experimental studies performed by other authors. They involve the data for vast ranges of geometry, mass velocity, heat flux rate, and pressure, in some cases for up- and downward flow, for flow with and without thermoacoustic oscillations. For the first time the data illustrating deterioration of heat transfer in the bundles of fuel elements are presented. An attempt to explain the phenomena of “inlet” peak of wall temperature is made. It is shown that temperature regimes of the tubes cooled with supercritical water in a gaseous state (i.e., at bulk temperature above the pseudocritical temperature) are close to linear, stable and easily reproducible within a wide range of mass and heat fluxes. Some requirements to the experimental setup, coolant quality, test sections etc. that should be followed in studying thermal and hydraulic parameters of supercritical coolant are analyzed.

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 categoriesInsufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score1.000

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)0.0010.001

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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.

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

Citations11
Published2013
Admission routes1
Has abstractyes

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