Comparison and analysis of bubble growth and foam formation models
Bibliographic record
Abstract
Purpose The main purpose of this paper is to present and compare two different models for bubble growth and foam formation and to conduct a thorough assessment in terms of their numerical implementation and prediction accuracy. Design/methodology/approach The two models are assessed and validated against experimental measurements. The first model is known as a single bubble growth model and treats the foaming process as a single bubble growing in a large pool with enough gas available for growth, while the second model (cell model) takes into account the finiteness of gas supply availability as well as the effects of surrounding bubbles. The models are based on the application of the conservation of continuity and momentum principles and on constitutive equations to represent the viscosity of the melt. The models are numerically implemented using a finite difference scheme and their predictions are compared against experimental measurements. Findings The results demonstrate that the single bubble model predicts an infinite bubble growth with time due to the assumption of unlimited supply of the blowing agent. Meanwhile the cell model gives an equilibrium bubble size because it accounts for gas depletion. From this work, it was concluded that the cell model is the best model that adequately describes experimental data. Practical implications The problem of bubble growth and foam formation is of great importance in the process industry as it plays a key role in diverse technological fields such as the production of foamed plastics. Originality/value The findings here are important for the appropriate modeling of bubble growth and foam formation and for scheduling and optimizing the process. A simple model will suffice for the early stage of the process while a cell model is more appropriate for the entire duration of the 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".