Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting
Sperl G. 2022. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria.
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Thesis
| PhD
| Published
| English
Author
Supervisor
Department
Series Title
ISTA Thesis
Abstract
The complex yarn structure of knitted and woven fabrics gives rise to both a mechanical and
visual complexity. The small-scale interactions of yarns colliding with and pulling on each
other result in drastically different large-scale stretching and bending behavior, introducing
anisotropy, curling, and more. While simulating cloth as individual yarns can reproduce this
complexity and match the quality of real fabric, it may be too computationally expensive for
large fabrics. On the other hand, continuum-based approaches do not need to discretize the
cloth at a stitch-level, but it is non-trivial to find a material model that would replicate the
large-scale behavior of yarn fabrics, and they discard the intricate visual detail. In this thesis,
we discuss three methods to try and bridge the gap between small-scale and large-scale yarn
mechanics using numerical homogenization: fitting a continuum model to periodic yarn simulations, adding mechanics-aware yarn detail onto thin-shell simulations, and quantitatively
fitting yarn parameters to physical measurements of real fabric.
To start, we present a method for animating yarn-level cloth effects using a thin-shell solver.
We first use a large number of periodic yarn-level simulations to build a model of the potential
energy density of the cloth, and then use it to compute forces in a thin-shell simulator. The
resulting simulations faithfully reproduce expected effects like the stiffening of woven fabrics
and the highly deformable nature and anisotropy of knitted fabrics at a fraction of the cost of
full yarn-level simulation.
While our thin-shell simulations are able to capture large-scale yarn mechanics, they lack
the rich visual detail of yarn-level simulations. Therefore, we propose a method to animate
yarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware
fashion in real time. Using triangle strains to interpolate precomputed yarn geometry, we are
able to reproduce effects such as knit loops tightening under stretching at negligible cost.
Finally, we introduce a methodology for inverse-modeling of yarn-level mechanics of cloth,
based on the mechanical response of fabrics in the real world. We compile a database from
physical tests of several knitted fabrics used in the textile industry spanning diverse physical
properties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation, using homogenized
shell models to speed up computation and adding some small-but-necessary extensions to
yarn-level models used in computer graphics.
Publishing Year
Date Published
2022-09-22
Publisher
Institute of Science and Technology Austria
Acknowledged SSUs
Page
138
ISBN
ISSN
IST-REx-ID
Cite this
Sperl G. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. 2022. doi:10.15479/at:ista:12103
Sperl, G. (2022). Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12103
Sperl, Georg. “Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:12103.
G. Sperl, “Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting,” Institute of Science and Technology Austria, 2022.
Sperl G. 2022. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria.
Sperl, Georg. Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:12103.
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