Numerical approaches and analysis of spray characteristics for pressuriser nozzles
Bibliographic record
Abstract
Abstract Volume of fluid (VOF) method combined with primary breakup model (PBM) is developed to model large flow rate pressure‐swirl nozzle used in pressuriser of the pressurised water reactor (PWR) power plant system. For the VOF model, the renormalisation‐group (RNG) K – ϵ model is selected to ensure the accuracy of simulation for swirling flows. The 3‐D transient flow is simulated and the dynamic stability of the injection pattern is analysed for the nozzle. Based on the growth rate of the disturbance at the interface of the two phases, PBM is embedded in the VOF model in the form of user defined functions (UDF) to investigate the further breakup from sheet to spray drop and predict the spray drop size spectrum. In addition, experimental studies, considering the characteristics of mass flow rate, spray cone angle, spray flux distribution and drop size spectrum, were conducted to verify accuracy of the numerical results. The result of comparison shows that there is good agreement between the simulated results and the experimental data. Especially, the distribution tendency of the spray drop size simulated by PBM fits the experimental value well and the drop size corresponding to the maximum probability is predicted successfully. The present investigation shows that the VOF–PBM methodology can be applied to simulate the pressure‐swirl nozzle and optimise design of spraying systems.
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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) | 0.000 | 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".