Impact of web permeability on high speed web transport
Bibliographic record
Abstract
Measurements of drag on a moving web in a multi-span festoon show a stronger than expected dependency on the porosity of the web with a highly non-linear relationship. The experiments suggest a wall shear stress 3-4 times larger than non-porous webs or historical Couette flow data for solid walls. Previous DNS studies of boundary layers with passive porous surfaces predict a much smaller increase in wall shear stress for a porous wall of only 40%. Other DNS studies of porous walls with periodic transpiration do show a large increase in drag under certain periodic conditions of modest amplitude. Although those results are more aligned in magnitude with this study, the exact reason for the observed high drag for porous webs in this present study is not understood because there was no external disturbance applied to the web. Previously reported experimental data reported a strong linear correlation between wall shear stress and web permeability. The present paper refines those measurements in more detail and shows a highly nonlinear relationship between wall shear stress and permeability, but we offer no physical explanation for this relationship. It can be hypothesized that natural flutter of the web results in a similar mechanism shown in the periodic DNS study, but when the natural flutter was reduced by increasing web tension, there was only a small decrease of the drag.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".