Macroscopic and mesoscopic simulation of viscoelastic free surface flow in gas‐assisted injection moulding process
Bibliographic record
Abstract
Abstract In this work, an improved simple coupled level‐set and volume of fluid (S‐CLSVOF) method is proposed to trace the moving interfaces in gas assisted injection moulding (GAIM) process based on the viscoelastic FENE‐P model. Firstly, the Kreisselmeier‐Steinhauser (KS) function constructed by means of Boolean operations is adopted as the shape level set (LS) function to precisely represent the complex mould cavities. Then, the benchmark problem of two‐dimensional deformation is used to verify the ability of the S‐CLSVOF method for capturing the moving interfaces. The stress birefringence is calculated and compared with the experiment result. Finally, the proposed method is further applied to the mould filling and gas penetration processes in GAIM. The macroscopic bubble appearance, temperature distribution, and the behaviour of the mesoscopic molecular orientation are shown and analyzed in detail. Due to the complexity of the gas‐liquid interaction, the phenomenon of asymmetrical gas flow is clearly observed in the gas penetration process. The influences of the macroscopic parameters on the macroscopic gas flows and mesoscopic molecular orientation are also discussed, such as insert positions, melt temperatures, and gas delay time. The numerical results illustrate that the coupled method can be applied to the multiscale numerical simulation of viscoelastic flows with complex free surfaces and provide significant guidance for the GAIM 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 | 0.000 |
| 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 | 0.001 | 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".