Wrinkling and folding of thin films by viscous stress
Bibliographic record
Abstract
We examine the buckling of a thin elastic film floating on a viscous liquid layer which is itself supported on a prestretched rubber sheet. Releasing the prestretch in the rubber induces a viscous stress in the liquid, which in turn induces a compressive stress in the elastic film, leading to buckling. Unlike many previous studies on wrinkling of floating films, the buckling process in the present study is dominated by viscous effects whereas gravitational effects are negligible. An approximate shear lag model predicts the evolution of the stress profile in the unbuckled film that depends on three parameters: the rate at which the prestretch is released, the thickness of the liquid layer, and the length of the elastic film. A linear perturbation analysis is developed to predict the wavelength of wrinkles. Numerical simulations are conducted to predict nonlinear evolution of the wrinkle wavelength and amplitude. Experiments using elastic polymer films and viscous polymer liquids show trends that are qualitatively consistent with the predictions although quantitatively, the experimentally-observed wrinkle wavelengths are longer than predicted. Although this article is focused only on small-strain wrinkling behavior, we show that application of large nominal strains (on the order of 100%) leads to sharply localized folds. Thus this approach may be useful for developing buckled features with high aspect ratio on surfaces.
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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 | 0.000 |
| 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 | 0.000 | 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".