Computer Simulation of Bubble-Growth Phenomena in Foaming
Bibliographic record
Abstract
This paper discusses the research conducted to achieve an accurate bubble-growth model and simulation scheme to describe precisely the bubble-growth phenomena that occur in polymeric foaming. Using the accurately measured thermophysical and rheological properties of polymer/gas mixtures (i.e., the solubility, the diffusivity, the surface tension, the viscosity, and the relaxation time) as the inputs for computer simulation, the growth profiles for bubbles nucleated at different times were predicted and carefully compared to experimentally observed data obtained from batch foaming simulation with online visualization. A polystyrene/carbon dioxide (PS/CO 2 ) system is used herein as a case example. It was verified that the cell-growth model is capable of thoroughly depicting the growth behaviors of bubble nuclei nucleated under varying processing conditions without using any fitting parameter. These results indicate that the established model accounts for most of the physics behind the bubble-growth phenomena. Furthermore, the effects of the aforementioned thermophysical and rheological parameters on the cell-growth dynamics were demonstrated by a series of sensitivity studies.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".