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Assessment of Heat-Transfer Correlations Against Experimental Data Obtained With Supercritical Water in Vertical Annuli

2012· article· en· W2153280078 on OpenAlexafffund
Zhendong Yang, Qincheng Bi, Han Wang, Gang Wu, L.K.H. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsAtomic Energy (Canada)
FundersNational Key Research and Development Program of ChinaAtomic Energy of Canada Limited
KeywordsSupercritical fluidHeat transfer coefficientHeat transferHeat fluxThermodynamicsMaterials scienceCritical heat fluxRange (aeronautics)Mass fluxMechanicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Eleven correlations proposed for supercritical heat-transfer coefficients were assessed against a set of experimental data obtained recently with supercritical water flow in a vertical annular test section at Xi’an Jiaotong University. The inner heated rod of the test section had an outer diameter of 8 mm, while the outer unheated tube had an inner diameter of 16 mm (resulting in a gap size of 4 mm). The experiment covered pressure range from 23 to 28 MPa, mass-flux range from 350 to 1000 kg/m2s, and heat-flux range from 200 to 1000 kW/m2. The assessment shows relatively good agreement between predicted and experimental heat-transfer coefficients for several correlations. Some discrepancies have been observed at the region where deteriorated heat transfer, and are attributed to the modified Dittus-Boelter formulation that captures mainly the normal heat-transfer region. Overall, the Dittus-Boelter correlation is shown applicable only for the normal heat-transfer region, and significantly overpredicts the heat-transfer coefficient at the deteriorated heat-transfer region. The correlation of Bishop et al. appears valid for the current experimental database, particularly for high mass fluxes.

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 score0.128
Threshold uncertainty score0.639

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.284
Teacher spread0.258 · 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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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