Heat-Transfer Correlations for Supercritical-Water and Carbon Dioxide Flowing Upward in Vertical Bare Tubes
Bibliographic record
Abstract
This paper presents an analysis of new heat-transfer correlations developed for SuperCritical Water (SCW) and SuperCritical Carbon Dioxide (SCCO2) flowing upward in vertical bare tubes. Previous studies have shown that existing correlations deviate significantly from experimental Heat-Transfer-Coefficient (HTC) values, especially, within the pseudocritical range for both fluids.Therefore, new empirical correlations based on the following approaches in terms of characteristic temperature were developed: 1) bulk-fluid-temperature approach (SCW); 2) wall-temperature approach (SCW and SCCO2); and 3) film-temperature approach (SCW). Analysis showed that for SCW the best correlations, i.e., the most accurate ones, are based on the bulk-fluid- and wall-temperature approaches. Calculated wall temperatures according to these new correlations were within ±15% and HTC values were within ±25% for analysed datasets. For SCCO2, the new correlation was within ±20% for wall temperatures and within ±30% for HTC values.The proposed correlations can be used for (1) calculations of SCW and SCCO2 heat-transfer in SuperCritical (SC) steam generators / heat exchangers; (2) preliminary heat-transfer calculations in reactors fuel channels as a conservative approach; (3) future comparisons with other independent datasets and with bundle data; (4) verification of computer codes for thermalhydraulics; and (5) verification of scaling parameters between SCW, SCCO2 and other SC fluids.Copyright © 2012 by ASME
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".