Numerical investigation on effects of fuel rod with different shapes of bow deformation on the subcooled boiling of coolant in a fuel assembly
Bibliographic record
Abstract
Abstract The Eulerian two‐phase boiling model of the subcooled boiling of coolant in a 3 × 3 fuel assembly is established and compared with the experimental data to verify its accuracy. The effects of four shapes of bow deformation on subcooled boiling flow and heat transfer characteristics are obtained by comparing and analyzing the distributions of thermal–hydraulic parameters, including the axial wall superheat, liquid phase temperature, axial void fraction, various heat fluxes, heat transfer coefficient, and turbulent kinetic energy. All shapes of bow deformation will lead to the redistribution of coolant among different subchannels, and the distributions of thermal–hydraulic parameters would be greatly affected. The bow deformation of fuel rod easily causes local boiling, which results in local high temperature of liquid phase and bubble accumulation, and a decrease in the area of high liquid phase heat transfer coefficient on the surface of bowing fuel rod. Additionally, the non‐uniform distribution of turbulent kinetic energy caused by bow deformation in different axial sections not only affects the heat transfer performance of coolant, but also causes the increment in pressure drop, which has negative effects on the safe operation of the nuclear reactor. This paper can provide data and theoretical support for engineering design.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".