AN ASSESSMENT OF ROUND TUBE CORRELATIONS FOR CONVECTIVE HEAT TRANSFER AT SUPERCRITICAL PRESSURE
Bibliographic record
Abstract
The objective of this study is to evaluate round-tube data-based correlations depending on their applicability conditions including heat transfer mode and fluid. The present assessment of correlations was performed against the round-tube databases for vertical upward flow of water and CO2 from the Canadian Nuclear Laboratories multifluid, multigeometry databank of supercritical heat transfer. To categorize the data according to a representative heat mode, various criteria for onset of heat transfer deterioration were proposed. However, there is no consensus in the literature on a single approach. Two popular, semi-empirical criteria were chosen for screening the data for buoyancy- and acceleration-induced heat transfer deterioration. The experimental conditions of the data were screened for specified ranges of the 2 criteria indicating heat transfer deterioration. However, in many cases, the corresponding heat transfer mode of the experimental data was not appropriately predicted. In light of this inadequacy, improving accuracy of this method was deemed necessary. Therefore, each of the 2 criteria was empirically modified twice, once for water and once again for CO2. This paper presents these 2 original modifications for each of the fluids. Ultimately, each experimental data point was categorized by the modified criteria into 1 of 2 heat transfer modes, either normal or deteriorated. In total, 21 round-tube correlations were selected and applied to the categorized databases of normal and deteriorated heat transfer for water and CO2. Details of the assessment results are presented in this paper in tables of uncertainty numbers for each of the correlations and databases and in graphs, comparing best-estimate correlations with representative experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".