Effects of Various Parameters on Supercritical-Pressure Heat Transfer and Limits for Heat Transfer Correlations Obtained in Vertical Bare Tubes
Bibliographic record
Abstract
Currently, there is a number of Generation-IV SuperCritical Water-cooled nuclear-Reactor (SCWR) concepts under development worldwide. These high temperature and pressure reactors will have significantly higher operating parameters compared to those of current water-cooled nuclear-power reactors (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, as the steam will be flowing directly to a steam turbine. In support of developing SCWRs studies are being conducted on heat transfer at SuperCritical Pressures (SCPs). Currently, there are very few experimental datasets for heat transfer at SCPs in power-reactor fuel bundles to a coolant (water) available in open literature. Therefore, for preliminary calculations, heat-transfer correlations developed with bare-tube data can be used as a conservative approach. Selected empirical heat-transfer correlations, based on experimentally obtained datasets, have been put forward to calculate Heat Transfer Coefficients (HTCs) in forced convective in various fluids, including water at SCPs. The Mokry et al. correlation (2011) has shown a good fit for experimental data at supercritical conditions within a wide range of operating conditions in Normal and Improved Heat-Transfer (NHT and IHT) regimes. However, it is known that a Deteriorated Heat-Transfer (DHT) regime appears in bare tubes earlier than that in bundle flow geometries. Therefore, it is important to know if bare-tube heat-transfer correlations for SCW can predict HTCs at heat fluxes beyond those defined as starting of DHT regime in bare tubes. The Mokry et al. (2011) correlation fits the best SCW experimental data for HTCs and inner wall temperature for bare tubes at SCPs within the NHT and IHT regimes. However, this correlation might have problems with convergence of iterations at heat fluxes above 1000 kW/m2.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".