Improvement of Supercritical Water Heat-Transfer Correlations for Vertical Bare Tubes
Bibliographic record
Abstract
Currently, there are a number of Generation IV SuperCritical Water-cooled nuclear Reactor (SCWR) concepts under development worldwide. These high temperature, high pressure reactors will have much higher operating parameters compared to current Nuclear Power Plants (NPPs) (i.e., steam pressures of about 25 MPa and steam outlet temperatures up to 625°C). Additionally, SCWRs will have a simplified flow circuit in which steam generators, steam dryers, steam separators, etc. will be eliminated. In support of developing these SCWRs, studies are currently being conducted for heat transfer at supercritical conditions. Currently, there are no experimental datasets for heat transfer at supercritical conditions from power-reactor fuel bundles to a coolant (water) available in open literature. Therefore, for preliminary calculations, heat-transfer correlations obtained with bare-tube data can be used as a conservative approach. A number of empirical generalized correlations, based on experimentally obtained datasets, have been proposed to calculate Heat Transfer Coefficients (HTCs) in forced convective heat transfer for various fluids, including water at supercritical pressures. There have been a number of methods applied to correlate heat transfer data. The most conventional approach is to modify the classical Dittus-Boelter correlation for forced convection. The Bishop et al. correlation is an example of this type modification with an addition of an entrance-region term. The Mokry et al. correlation (2009) was developed as a Dittus-Boelter-type correlation with thermophysical properties taken at a bulk-fluid temperature. The derived correlation has shown a good fit for experimental data at supercritical conditions within a wide range of operating conditions in normal and improved heat-transfer regimes. This correlation has an uncertainty of about ±25% for HTC values and about ±15% for calculated wall temperature. However, this correlation does not take into account the entrance-region effect. The objective of this paper is an investigation of the entrance-region effect to be incorporated into the proposed Mokry et al. correlation (2009) in an attempt to further improve its accuracy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".