Bibliographic record
Abstract
Modeling flash evaporation cases is challenging due to their occurrence in high temperatures and mass flow rates, along with mass transfer taking place in a narrow region of space. As an improvement to the previous Limited Evaporation model, which was based on the normalized critical work of nucleation, a new Bulk Nucleation model based on Classical Nucleation theory is developed and tested in comparison with liquid–vapor evaporation experiments conducted at the Brookhaven National Laboratories. The original theory is modified to take into account the cluster size formed during nucleation and a minimum threshold of vapor volume fraction required to trigger large-scale mass transfer. The Bulk Nucleation model shows better predictions for radial volume fractions, representing an improvement over well-correlated predictions for area-averaged pressure and volume fractions. The discrepancy in the radial volume fractions is attributed to the presence of pressure taps used in experiments, which protrude into the fluid domain. The role of model parameters such as cluster size and vapor volume fractions in the mass transfer model is also discussed in detail in this study. Similar to previous work, the new model is implemented in the open-source Computational Fluid Dynamics solver OpenFOAM in an Euler–Euler framework, which provides for the use of inter-momentum forces such as lift, drag, and turbulent dispersion, which are essential for accurate predictions for transport and generation of new vapor bubbles. • A numerical phase change model based on classical nucleation theory. • Decay rates based on cluster size. • Metastability during phase change is captured by the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".