Progress in Experimental and Theoretical Evaluation Methods for Textile Permeability
Bibliographic record
Abstract
A great amount of attention has been given to the evaluation of the permeability tensor and several methods have been implemented for this purpose: experimental methods, as well as numerical and analytical methods. Numerical simulation tools are being seriously developed to cover the evaluation of permeability. However, the results are still far from matching reality. On the other hand, many problems still intervene in the experimental measurement of permeability, since it depends on several parameters including personal performance, preparation of specimens, equipment accuracy, and measurement techniques. Errors encountered in these parameters may explain why inconsistent measurements are obtained which result in unreliable experimental evaluation of permeability. However, good progress was done in the second international Benchmark, wherein a method to measure the in-plane permeability was agreed on by 12 institutes and universities. Critical researchers’ work was done in the field of analytical methods, and thus different empirical and analytical models have emerged, but most of those models need to be improved. Some of which are based on Cozeny-Karman equation. Others depend on numerical simulation or experiment to predict the macroscopic permeability. Also, the modeling of permeability of unidirectional fiber beds have taken the greater load of concern, whereas that of fiber bundle permeability prediction remain limited. This paper presents a review on available methods for evaluating unidirectional fiber bundles and engineering fabric permeability. The progress of each method is shown in order to clear things up.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".