ICONE19-43876 UPDATED HEAT TRANSFER CORRELATIONS FOR SUPERCRITICAL WATER-COOLED REACTOR APPLICATIONS
Bibliographic record
Abstract
In support of the development of SuperCritical Water-cooled Reactors (SCWRs), research is currently being conducted for heat-transfer at supercritical conditions. Currently, there are no experimental datasets for heat transfer from power reactor fuel bundles to the fuel 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 large set of experimental data, for supercritical water was analyzed and an updated heat-transfer correlation for forced-convective heat-transfer, in the normal heat transfer regime, was developed. This experimental dataset was obtained within conditions similar to those for proposed SCWR concepts. Thus, this new correlation can be used for preliminary heat-transfer calculations in SCWR fuel channels. It has demonstrated a good fit for the analyzed dataset. Experiments with SuperCritical Water (SCW) are very expensive. Therefore, a number of experiments are performed in modeling fluids, such as carbon dioxide and refrigerants. However, there is no common opinion if SC modeling fluids' correlations can be applied to SCW and vice versa. Therefore, a correlation for supercritical carbon dioxide heat transfer was developed as a less expensive alternative to using supercritical water. The conducted analysis also meets the objective of improving our fundamental knowledge of the transport processes and handling of supercritical fluids. These correlations can be used for supercritical water heat exchangers linked to indirect-cycle concepts and the cogeneration of hydrogen, for future comparisons with other independent datasets, with bundle data, for the verification of computer codes for SCWR core thermalhydraulics and for the verification of scaling parameters between water and modeling fluids.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.018 |
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".