Numerical simulation of polymer filling process by a combined finite element/discontinuous Galerkin/level set method
Bibliographic record
Abstract
Abstract The filling process in injection molding involves polymer melt and air with large density/viscosity ratios and the transient free surface. It is treated as a challenging viscoelastic‐Newtonian two‐phase flow problem especially on irregular domains. In this paper, the complex filling process for the irregular cavity is simulated using a combined finite element/discontinuous Galerkin/level set method with application to the socket with five inserts, which is rarely investigated. The rheological behaviour of the viscoelastic fluid is predicted according to the eXtended Pom‐Pom (XPP) constitutive model. The level set method proposed from prior literature is utilized to capture the moving interface because of its simplicity and efficiency. The hybrid continuous and discontinuous Galerkin method is utilized to solve the viscoelastic incompressible Navier‐Stokes equations. The second order Runge Kutta discontinuous Galerkin (RKDG) method is employed to deal with the XPP constitutive equation and the level set equation due to their hyperbolic natures. This combined algorithm is convenient to cope with the irregular cavity and avoid any stabilization terms. We first investigate the filling process of the rectangular cavity without and with a diamond insert and compare with other numerical and experimental results to illustrate the validity of the coupled method. Moreover, the cavity of the socket with five inserts is considered an application case. We analyze the influences of the inlet velocity and elasticity on physical quantities such as stresses, stretch, etc. The simulation results could provide some numerical predictions for the polymer industry.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".