In-plane permeability prediction model for non-crimp and 3D orthogonal fabrics
Bibliographic record
Abstract
Permeability reflects the ease of flow inside a composite fabric. A predictive model has been developed to estimate the unidirectional permeability in both, the warp and weft directions, for a family of non-crimped and 3D orthogonal fabrics. The model is based on an analytical solution derived from previous studies, in which the microscopic permeability of unidirectional fiber bundles is estimated. The implementation of this model requires basic geometrical parameters of the fabric architecture. Those parameters include the dimension of the mesopores and architecture of the fiber bundles, which are determined from pictures taken for the fabric and from the textile data sheet. In addition, the average volume of mesopores and fiber bundles are calculated for different fiber volume fractions in the warp and weft directions. The model evaluates two contributions; the first one deals with the flow inside and in between the tows, while the second one figures out the flow deviations arising from the stitching yarns. The model uses effective radius and fiber volume fraction to evaluate permeability for the two flow contributions mentioned above. An experimental investigation validates the predictive model for five different fabrics and three different fiber volume fractions. Good agreement is found.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".