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Record W3211872649 · doi:10.1115/imece2000-1518

Some Aspects of Critical-Heat-Flux Enhancement in Tubes

2000· article· en· W3211872649 on OpenAlexaff
S. S. Doerffer, D.C. Groeneveld, K. F. Rudzinski, Igor Pioro, Jonathan W. Martin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsCritical heat fluxMaterials scienceFlux (metallurgy)Flow (mathematics)FreonInsert (composites)Heat fluxMechanicsHeat exchangerThermodynamicsComposite materialHeat transferPhysicsMetallurgy

Abstract

fetched live from OpenAlex

Abstract This paper summarizes the effects of various types or numbers of critical-heat-flux (CHF)-enhancing inserts in tubular geometries. The impact of inserts on CHF is frequently expressed by an enhancement ratio K: the ratio of CHF with an insert to the CHF in a bare tube for the same local flow conditions. The impact on K of the following parameters was investigated: (i) fluid type (Freon-134a, water), (ii) axial spacing between inserts, (iii) shape of the insert, (iv) flow blockage of the insert, (v) number of similar/dissimilar insert planes upstream, and (vi) impact of flow conditions. The spacing and flow-obstruction area were found to be the major geometric factors that affected K: by decreasing the relative spacing, L/D, to 16, K can reach a value of from 2 to 3, depending on the flow-obstruction area. Among flow parameters, the critical quality, xc, usually has a strong effect on K: K can increase from a value of 1 to 3, when xc increases from 0 to 0.4 for a mass flux G ≥ 2 Mg/m2s. For G < 2 Mg/m2s, CHF enhancement can disappear or become negative (K < 1). No cumulative effect was found on K for a series of upstream insert planes. CHF enhancement does not depend on fluid type, provided that the conditions in the fluids meet the CHF fluid-to-fluid modelling requirements.

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 score0.369
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)0.0010.000

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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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

Citations8
Published2000
Admission routes1
Has abstractyes

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