MétaCan
Menu
Back to cohort
Record W2080120971 · doi:10.1115/imece2002-39314

Heat Transfer to Water-Oxygen Mixtures at High Pressure

2002· article· en· W2080120971 on OpenAlexafffund
Steven N. Rogak, Sanja Boskovic, D. Faraji

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercritical fluidThermodynamicsHeat transfer coefficientHeat transferNucleate boilingCritical heat fluxMaterials scienceConvective heat transferOxygenDistilled waterHeat fluxChemistryChurchill–Bernstein equationTurbulenceNusselt number

Abstract

fetched live from OpenAlex

The constant pressure heat capacity and forced convection heat transfer coefficient was measured in a horizontal, smooth, electrically-heated tube. For the supercritical pressures considered, flow rates and temperatures (330–430 °C), the flow in the 6.2 mm ID tube was fully turbulent. The fluid was distilled water and up to 9 wt% oxygen. This mixture and the experimental conditions are found in supercritical water oxidation systems. At subcritical temperatures, the oxygen and water are almost immiscible, but just below the critical temperature, the fluid becomes single-phase. By measuring bulk and surface temperatures, knowing the mass and heat flux, both the heat capacity and heat transfer coefficient could be measured. The water-oxygen system is a highly non-ideal mixture, and small amounts of oxygen significantly reduce the temperature at which maximum heat transfer occurs. The changes in heat capacity appear to dominate the effect of oxygen on heat transfer, however, the mixtures do exhibit heat transfer deterioration at slightly subcritical temperatures, at flows and heat fluxes for which pure water shows nothing similar.

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 categoriesInsufficient payload (model declined to judge)
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 score0.042
Threshold uncertainty score0.999

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)0.0130.002

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 head0.010
GPT teacher head0.183
Teacher spread0.173 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2002
Admission routes2
Has abstractyes

Explore more

Same topicSubcritical and Supercritical Water ProcessesFrench-language works237,207