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Record W4404352497 · doi:10.48550/arxiv.2411.01616

Wrinkling and imaging of thin curved sheets

2024· preprint· en· W4404352497 on OpenAlexfundno aff
Megha Emerse, Lucas Goehring

Bibliographic record

VenuearXiv (Cornell University) · 2024
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsMaterials scienceGeology

Abstract

fetched live from OpenAlex

Thin films or sheets subjected to external forces often undergo mechanical instability, leading to regular patterns of wrinkles, folds, and creases. As can be anticipated from the difficulty of flattening a curved globe, any natural curvature of the sheet will have a strong influence on these instabilities. Here, we develop a non-invasive synthetic schlieren imaging technique to image and reconstruct the surface of wrinkling curved sheets, confined to float on water. Our method circumvents the small-amplitude limit of related imaging techniques, and we demonstrate robust means to estimate the reconstruction accuracy. We then evaluate how the sign and magnitude of Gaussian curvature affects the wrinkling of thin curved sheets, and compare observations of the wrinkle wavelength, amplitude and domain structure with recent theoretical predictions. While generally validating model predictions, we find that the assumption of a conserved surface area during wrinkling should be treated with some care. The control of wrinkling behavior demonstrated here can have application in the design of liquid lenses, microfluidics, active textured surfaces, and flexible electronic components.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.165
Teacher spread0.133 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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