Investigation of the steady state subcooled boiling regime in the hot subchannel of a VVER-1200 reactor core: A CFD analysis
Bibliographic record
Abstract
A Computational Fluid Dynamics (CFD) analysis is carried out on a three-dimensional subchannel representing the most intensive fuel assembly to simulate the thermal-hydraulic performance of the VVER-1200 hot channel under steady-state operation. It is found that, subcooled boiling is predicted for both the rated and the conservative operational parameters with the intensity of bubble generation found to be higher for the conservative scenario compared with the rated one. The predicted boiling phenomenon at the cladding surface may result in the formation of deposits, and pits, and may impair the strength of the fuel elements, and increase the inhomogeneity in fuel burnup. Therefore; a thorough investigation of the predicted boiling regime is required to shed light on the development of this phenomenon in a typical reactor subchannel using CFD tools. The CFD work conducted in this work is based on the Eulerian multiphase model in ANSYS in conjunction with the RPI wall boiling model. The developed model has been validated against other one-dimensional models found in the literature. The variation of mass-averaged quantities (across the channel) along the subchannel generally agrees with the 1D models, which builds confidence in the modeling approach. Contours showing the spatial variations of temperatures, vapor void ratio, as well as heat fluxes are presented. Profiles of the different components of heat flux during boiling are presented. It has been found that the heat flux variations during boiling pass through three distinct regimes. In the first, a steady increase in quenching and evaporation heat flux is observed, while convective heat flux declines. In the second regime, the quenching and evaporation heat fluxes plateau with a slight decrease, while the convective heat flux becomes zero. This signifies that the outer clad surface is largely covered with vapor bubbles and the only heat transfer mechanisms are due to quenching and evaporation. During the last regime, quenching and evaporation heat fluxes decline due to the decline of the imposed heat flux at the clad surface, and hence convective heat transfer starts to increase. In addition, profiles of the mass-averaged coolant temperature, void fraction, and outer clad surface temperatures along the hot element underrated and conservative operating conditions are also generated.
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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 | 0.000 | 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 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".