Estimation of turbulent energy mixing factor in <scp>PWR</scp> sub‐channel by <scp>DNS</scp>
Bibliographic record
Abstract
Abstract Turbulent mixing within sub‐channels plays a crucial role in understanding the thermal hydraulics of reactor channels. It serves as an empirical parameter in sub‐channel analysis and has long been a challenge in the nuclear industry. Conducting experiments in this context is challenging due to the stringent requirement of maintaining pressure balance among sub‐channels to prevent convection effects. Fortunately, direct numerical simulation (DNS) is emerging as an invaluable tool for addressing this persistent issue. DNS enables the direct computation of turbulent mixing by analyzing fluctuating lateral velocities, offering a more profound understanding of the underlying phenomena. In this study, DNS was conducted at six Reynolds numbers ranging from 17,640 to 1.5 × 105 in pressurized water reactor (PWR) geometry to investigate the lateral mixing driven by turbulence. By studying intricate mechanisms governing the turbulent mixing, the valuable insights into reactor thermal performance and safety are provided. Furthermore, a correlation for turbulent mixing of energy based on the DNS data has been derived, enhancing our ability to model and predict this critical aspect of reactor behaviour. Additionally, this paper explores temperature fluctuations occurring at the fuel rod surface due to turbulence. A probabilistic distribution for temperature fluctuation under specific reactor conditions is presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".