Comparison of Heat-Transfer Correlations Obtained in Supercritical-Water-Cooled Bundles
Bibliographic record
Abstract
SuperCritical Water-cooled Reactor (SCWR) as one of the six Generation-IV nuclear-power-reactor concepts will have increased thermal efficiency compared to that of current Nuclear Power Plants (NPPs) equipped with water-cooled reactors by operating the reactor coolant at supercritical conditions: Coolant pressure of about 25 MPa, inlet temperatures between 300–350°C, and outlet temperatures between 550–625°C. The major flow geometry inside the reactor core is the bundle flow geometry. For safe and efficient operation of an SCWR heat transfer coefficients should be calculated with minimum uncertainties. Unfortunately, the vast majority of experimental datasets were obtained in vertical bare tubes cooled with SCW. Experiments in a bundle flow geometry are even more complicated and expensive compared to that in bare tubes. Due to this very few experiments have been performed in bundles. According to the abovementioned, the vast majority of heat-transfer correlations are based on bare-tube data, and only one currently known correlation is based on a 7-element bundle cooled with SCW (the so-called, Dyadyakin and Popov correlation (1977)). Rods in this bundle are equipped with four helical ribs to enhance the heat transfer. However, the authors have not provided any dataset(s) associated with this bundle and correlation. In the current paper a number of bare-tube heat-transfer correlations obtained in SCW and the Dyadyakin and Popov correlation were compared with two datasets obtained in an annular channel with the heated central rod and 3-element bundle. The central rod in this annular channel and rods in the 3-element bundle have the same heated length as those in the 7-element bundle tested by Dyadyakin and Popov in 1977, and are also equipped with four helical ribs. The comparison showed that the Jackson correlation (2002) is the most accurate one in predicting Heat-Transfer-Coefficient (HTC) profiles in the annular channel at normal heat-transfer regime. The Dittus and Boelter correlation (1930) is the most accurate in predicting HTC profiles in the 3-element bundle at normal heat-transfer regime. No one correlation is capable to follow closely HTC profiles at the deteriorated heat-transfer regimes in both flow geometries. Aloo, it should be mentioned that bare-tube heat-transfer correlations, which have thermophysical properties based on bulk-fluid and wall temperatures, might have problems with convergence at high heat fluxes, i.e., above the heat flux at which the deteriorated heat-transfer regime starts in bare tubes.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".