Numerical Simulation of Steam Bubble Condensation Using Thermal Phase Change Model
Bibliographic record
Abstract
Evaporation and condensation phenomena play a significant role in many of the nuclear, biochemical, and thermal processes in industrial applications.It is a complicated phenomenon as it undergoes both heat and mass transfer processes along with the complexities involved in the interfacial regions of vapor and liquid phases.Several experimental works have been carried out in the recent past to understand the condensation process in detail.However, understanding the phenomenon using computational technique is more advantageous as the interfacial mass transfer between gas and liquid can be modelled accurately.In the present work, condensation of a saturated vapor bubble in the sub-cooled liquid is studied, and various factors that influence the bubble shape change and the bubble lifetime, are evaluated.The analysis is carried out using the 'Multi-Fluid Volume of Fluid' and 'Thermal Phase Change' (TPC) models implemented in ANSYS Fluent commercial CFD solver.A detailed study is performed to obtain the best approach for calculating interfacial area density using a 'user-defined function' (UDF), and the advantage of the node-based gradient calculation method is exhibited.The numerical results obtained for the history of bubble shape and bubble lifetime are validated against the experiment and previously published works with good accuracy.The paper also elaborates on how the initial bubble diameter, the subcooling temperature, and the system pressure affects the shape and lifetime of the bubble during the condensation process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".