Coupled Non-Conforming Finite Element and Finite Difference Approximation Based on Laminate Extrapolation to Simulate Liquid Composite Molding Processes. Part II: Non-Isothermal Filling and Curing
Bibliographic record
Abstract
A numerical methodology is presented for the accurate simulation of non-isothermal mold filling in Liquid Composite Molding.Starting with a mid-surface mesh and based on the structure of the fabric reinforcement, the extrapolation algorithm described in /17/ is used to generate 3D finite elements or parallel layers of 2D elements.To solve the coupled flow/energy/chemical species equations, two methodologies are used.The finite element formulation presented in /17/ is first coupled with a 3D finite element formulation for heat and curing analyses.In the second approach, a hybrid finite element and finite difference model is proposed.As the heat conduction and convection is evaluated in the plane of the elements by the finite element formulation, the through-thickness heat flow is computed by a one-dimensional finite difference approximation.The combination of the threedimensional mesh extrapolated in parallel layers with the thermal formulations allows investigating diverse degrees of coupling between the fluid and heat flows.With the same initial 2D mesh and material data, the user can easily define the level of complexity to be used in the numerical model.The results of the thermal formulations are validated with analytical tests.The performance of the model as well as the gain in cpu time are summarized.Finally a test case is presented to illustrate the advantages of the coupled model in terms of accuracy
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".