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  Structure Tensors for Dispersed Fibers in Soft Materials

Kalhöfer-Köchling, M., Bodenschatz, E., & Wang, Y. (2020). Structure Tensors for Dispersed Fibers in Soft Materials. Physical Review Applied, 13: 064039. doi:10.1103/PhysRevApplied.13.064039.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-AC83-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-AC84-3
Genre: Journal Article

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Kalhöfer-Köchling, M.1, Author              
Bodenschatz, E.2, Author              
Wang, Y.2, Author              
Affiliations:
1Laboratory for Fluid Physics, Pattern Formation and Biocomplexity, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063287              
2Laboratory for Fluid Dynamics, Pattern Formation and Biocomplexity, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063287              

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 Abstract: Soft tissues, such as skin, myocardium, and chordae tendineae, typically display anisotropic mechanical behavior due to their fibrous nature. In constitutive modeling, fiber families frequently are assumed to be unidirectional. Recent numerical results, however, display the need to incorporate dispersion of fiber orientation. This evidence gets supplemented by new experimental results based on high-resolution second-harmonic imaging microscopy. Generalized structure-tensor (GST) models are frequently utilized to model fiber dispersion, as they are mathematically easy to treat and demand only a little effort to implement. They can be regarded as Taylor-series expansions of the numerically more challenging angular-integration (AI) method, which encompasses a distribution of fiber orientations together with the associated fiber stress. In this work, we show how low-order GST models give rise to numerical instabilities as they show strong sensitivity with regards to the mean fiber orientation. To overcome these instabilities, we propose a different class of GST models, termed squared GST (SGST), which computes faster, is easier to implement, and converges to the AI faster than previous GST models of similar order. The SGST models promise to be adaptable to generalized problems, such as functional decomposition of fiber density as well as coupling between different fiber families. Advanced simulations with the proposed models will shed light on the complex behavior of fiber reinforced soft materials.

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Language(s): eng - English
 Dates: 2020-06-16
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1103/PhysRevApplied.13.064039
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Title: Physical Review Applied
Source Genre: Journal
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Pages: 12 Volume / Issue: 13 Sequence Number: 064039 Start / End Page: - Identifier: -