ICONE23-2018 INNOVATIVE APPROACH TO CORRELATE HEAT-TRANSFER DATA TO SUPECRITICAL CARBON DIOXIDE FLOWING UPWARD IN A BARE TUBE
Bibliographic record
Abstract
Existing literature on problems of heat transfer to fluids at supercritical pressures distinguishes three heat-transfer regimes: 1) Deteriorated, 2) normal, and 3) enhanced. Conventional approach to correlate forced-convective heat transfer to supercritical fluids is applied to the normal and enhanced heat-transfer regimes. Although, there were recent attempts to correlate data for the deteriorated heat-transfer regime at mixed convection, currently there are no heat transfer correlations for DHT occurring at pure forced convection at high heat fluxes. Thus, an innovative approach was developed to correlate data without distinguishing heat transfer regimes. The approach is discussed in this paper. Using this approach, the heat transfer data to supercritical CO2 in forced convection regime, which were obtained at MR-1 Loop at Chalk River Laboratories, were correlated with an RMS of approximately 10% (corresponds to approximately 20% spread based on the 2σ-level). This result is twice less than that previously obtained at the University of Ontario Institute of Technology. The paper covers the following major topics: thermal properties of supercritical fluids, specifics of heat transfer to fluids at supercritical state, overview of existing types of correlations, conventional methodology for the development of heat transfer correlations, and innovative methodology for the development of heat transfer correlations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".