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Record W3093762173 · doi:10.1115/icone2020-16456

Numerical Investigation on Heat Transfer to Supercritical Water Flowing Upward in a 4-M Long Bare Vertical Circular Tube

2020· article· en· W3093762173 on OpenAlexaff
Dong Yang, Qixian Wu, Lin Chen, Igor Pioro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSupercritical fluidHeat transferBuoyancyMechanicsMaterials scienceHeat fluxThermodynamicsHeat transfer enhancementInletCritical heat fluxTube (container)Flow (mathematics)Heat transfer coefficientPhysicsMechanical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Thermal efficiency and safety of Generation-IV nuclear-power-reactor concept - Supercritical Water-cooled Reactor (SCWR) depend on solid knowledge of specifics of SCW thermophysical properties and heat transfer within these conditions. As a preliminary, but conservative approach to uncover these specifics is analysis of experimental data obtained in bare tubes including numerical investigation. This paper presents the numerical investigation, based on computational fluid dynamics, of the heat-transfer characteristics of SCW flow in a 4-m long circular tube (ID = 10 mm). The flow and heat-transfer mechanism of SCW in the vertical tube under the influence of buoyancy and flow acceleration are analyzed. Results of numerical simulation predict the experimental data with reasonable accuracy. The results indicated that in the region of q/G > 0.4 kJ/kg, the wall temperature distribution tends to be non-linear, and heat transfer may deteriorate. When Tb < Tpc < Tw, internal wall temperature shows peaks, which corresponds to heat-transfer deterioration. Meanwhile the position, where the deterioration occurs is continuously moved forward to the inlet as the heat flux increases. Velocity changes near the wall show an M shape according to mass conservation for the density change.

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 score0.148
Threshold uncertainty score0.887

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 head0.023
GPT teacher head0.218
Teacher spread0.196 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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