Analysis of dynamic flows through porous media. Part I: Comparison between saturated and unsaturated flows in fibrous reinforcements
Bibliographic record
Abstract
Abstract This article presents a general approach to model flows through unsaturated porous media as they occur in Liquid Composite Molding (LCM). Saturated and unsaturated flows will be studied here both from the experimental and theoretical points of view. It is indeed important to distinguish between these two flow behaviors in order to understand the interactions between the three phases that coexist in a fibrous reinforcement: the solid and fluid phases on one hand, and the air content on the other. The experimental work presented here includes the study of permanent and transient flow regimes, both for saturated and unsaturated porous media. The dynamic effects that occur during fluid injection through fibrous reinforcements highlight the double scale, structure of their pore volume. The ratio between saturated and unsaturated permeabilities appears to be connected to the degree of saturation and to the porosity of the part. Given the importance of permeability as a key input parameter in process simulation, this article proposes to introduce the degree of saturation in the equations that govern the flow in order to increase the accuracy of numerical predictions. This will not only provide a better understanding of the underlying physical phenomena during the fluid impregnation of a fibrous preform, but will also ultimately allow the study of air entrapment mechanisms that govern the quality of composite parts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".