Publisher DOI: | 10.1016/j.commatsci.2021.110711 | Title: | Computational study of the geometric properties governing the linear mechanical behavior of fiber networks | Language: | English | Authors: | Davoodi Kermani, Iman Schmitter, Maximilian Eichinger, Jonas Aydin, Roland C. Cyron, Christian J. |
Keywords: | Descriptors; Fiber network; Representative volume element; Simulated annealing | Issue Date: | Nov-2021 | Source: | Computational Materials Science 199: 110711 (2021-11) | Abstract (english): | Materials whose microstructure is formed by random fiber networks play an important role both in biology and engineering. So far, it still remains unclear which geometric properties of the fiber network determine the macroscopic mechanical properties of such materials. This paper presents a computational study based on a large number of representative volume elements of random fiber networks. Our study reveals that the linear mechanical properties of fiber networks (i.e., Young's modulus and Poisson's ratio) are largely determined by only four scalar key descriptors. These are the number of fibers per volume, the mean node valency, the mean fiber length, and the mean direction cosine between fibers adjacent to the same node. Number of fibers per volume and node valency were found to be responsible for around 80% of the variance of the mechanical properties, making them the two by far dominant microstructural descriptors. In the part of the configuration space covered by our study, we observed a linear or quadratic relationship between the above four scalar microstructural descriptors and the Young's modulus. For the number of fibers per unit volume we propose a theoretical explanation for this simple relation. |
URI: | http://hdl.handle.net/11420/10107 | ISSN: | 0927-0256 | Journal: | Computational Materials Science | Institute: | Kontinuums- und Werkstoffmechanik M-15 | Document Type: | Article | Project: | SFB 986: Teilprojekt B09 - Mikrostrukturbasierte Klassifizierung und mechanische Analyse nanoporöser Metalle durch maschinelles Lernen |
Appears in Collections: | Publications without fulltext |
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