Re-Meshing Algorithms Applied to Mould Filling Simulations in Resin Transfer Moulding
Bibliographic record
Abstract
In injection moulding processes such as Resin Transfer Moulding (RTM) for example, numerical simulations are usually performed with a fixed mesh, on which the displacement of the flow front is predicted by the numerical algorithm. During the injection, special physical phenomena occur on the front, such as capillary effects inside the fibre tows or heat transfer when the fluid is injected at a different temperature than the mould. In order to approximate these phenomena accurately, it is always better to adapt the mesh to the shape of the flow front. This can be achieved by implementing re-meshing algorithms, which will provide not only more accurate solutions, but also faster calculations. In order to represent precisely the shape of the saturated domain in the cavity, the mesh needs to be non-isotropic in the vicinity of the flow front. The size of the elements along the front is connected to the overall accuracy needed for the simulation; the size in the perpendicular direction governs the accuracy on the position of the moving boundary in time. Since these two constraints on element size are not related, the need for non-isotropic mesh refinement is crucial. In the approach proposed here, the mesh is changed at each time step from a background isotropic mesh used as starting point in the refinement algorithm. The solution needs to be projected on the new mesh after each re-meshing. This amounts to adopting a new filling algorithm, which will be validated by comparison to a standard simulation (without re-meshing) and with experimental data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".