Development of a Heat-Transfer Correlation for Supercritical Water Flowing in a Vertical Bare Tube
Bibliographic record
Abstract
This paper presents an analysis of heat-transfer to SuperCritical Water (SCW) in bare vertical tubes. A large set of experimental data, obtained in Russia, was analyzed and a new heat-transfer correlation for SCW was developed. This experimental dataset was obtained within conditions similar to those for proposed SuperCritical Water-cooled nuclear Reactor (SCWR) concepts. Thus, the new correlation presented in this paper can be used for preliminary heat-transfer calculations in SCWR fuel channels. The experimental dataset was obtained for SCW flowing upward in a 4-m-long vertical bare tube. The data was collected at pressures of about 24 MPa for several combinations of wall and bulk-fluid temperatures that were below, at, or above the pseudocritical temperature. The values ranged for mass flux from 200–1500 kg/m2s, for heat flux up to 1250 kW/m2 and for inlet temperatures from 320 to 350°C. Previous studies have confirmed that there are three heat-transfer regimes for forced convective heat transfer to water flowing inside tubes at supercritical pressures: (1) Normal Heat-Transfer (NHT) regime; (2) Deteriorated Heat-Transfer (DHT) regime, characterized by lower than expected Heat Transfer Coefficients (HTCs) (i.e., higher than expected wall temperatures) than in the NHT regime; and (3) Improved Heat-Transfer (IHT) regime with higher-than-expected HTC values, and thus lower values of wall temperature within some part of a test section compared to those of the NHT regime. Also, previous studies have shown that the HTC values calculated with the Dittus-Boelter and Bishop et al. correlations deviate quite substantially from those obtained experimentally. In particular, the Dittus-Boelter correlation significantly over predicts the experimental data within the pseudocritical range. A new heat-transfer correlation for forced convective heat-transfer in the NHT regime to SCW in a bare vertical tube is presented in this paper. It has demonstrated a relatively good fit for HTC values (±25%) and for wall temperature calculations (±15%) for the analyzed dataset. This correlation can be used for supercritical water heat exchangers linked to indirect-cycle concepts and the co-generation of hydrogen, for future comparisons with other independent datasets, with bundle data, as the reference case, for the verification of computer codes for SCWR core thermalhydraulics and for the verification of scaling parameters between water and modeling fluids.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".