Numerical and experimental investigation of saturated transverse permeability of 2D woven glass fabrics based on material twins
Bibliographic record
Abstract
Abstract The transverse permeability of fibrous reinforcement is one of the critical parameters that govern fabrication efficiency and production quality in several liquid composite molding process variants devised to achieve transverse impregnation of fibrous reinforcements. It is difficult to precisely measure and predict the transverse permeability, because it is simultaneously affected by diverse factors, for example, the geometric features of the test mold, nesting between fabric layers, and flow‐induced compaction of the fiber bed. In this article, the saturated transverse permeability of 2D woven glass fabrics is investigated using information provided by mesostructural geometric models reproducing the real textile architecture. These models are created by micro‐CT aided geometric modeling, a recently proposed technique to analyze three‐dimensional images obtained by X‐ray microtomography. They are called “material twins” because they reproduce with assessed accuracy the geometrical configuration of the textile preform, are representative of material variability, and allow performing numerical simulations of flow or mechanical properties. Computer simulations of steady state transverse flows in material twins were carried out to evaluate the transverse permeability and compared to experiments. Issues concerning material variability due to nesting and the accuracy of transverse permeability measurements were considered and discussed. A good agreement was obtained between numerical and experimental values of transverse permeability. Both approaches show a significant influence of the number of layers considered, which can be explained by nesting between adjacent plies. Numerical simulations also illustrate how nesting significantly affects material variability.
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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 | 0.000 | 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) | 0.001 | 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".