Bibliographic record
Abstract
As resin transfer molding (RTM) is being increasingly used to manufacture composite parts, there is a strong interest to understand the basic physical phenomena that occur at each stage of the process. Modeling and simulation play an important role in the development and optimization of molds production and in devising appropriate resin injection strategies. In general, process simulation requires preprocessing to be carried out carefully to conduct successful, reliable, and reasonably fast calculations. However, it can be time consuming at the early stages of mold design to run several simulations with minor changes only in the geometrical model or in the mesh in order to optimize the mold or some given operating conditions. Unfortunately, in that case the entire mesh has usually to be regenerated. This paper presents applications of a remeshing algorithm to RTM flow simulation. It illustrates how remeshing techniques can be used to enhance the automatic meshing capability by including injection ports and channels along the mold boundaries or along the interior injection lines. Remeshing can also be used to smooth the resin front during mold filling. In the latter case, mesh refinement is based on the flow front without attempting to minimize computational error. By adapting the mesh anisotropy to the flow front during mold filling, the shape of the advancing flow front can be more closely approximated. The first part of this paper describes the remeshing algorithm and the associated anisotropic metric. Then, several examples of metrics are given to provide guidelines for application engineers and also to illustrate their practical implementation. After introducing the RTM process and recalling the basic equations that govern mold filling, local remeshing of injection ports and runners are presented, followed by the application that minimizes front smearing.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".