Topology and Toughening of Sparse Elastic Networks

被引:17
|
作者
Yamaguchi, Tetsuo [1 ]
Onoue, Yudai [1 ]
Sawae, Yoshinori [1 ,2 ]
机构
[1] Kyushu Univ, Dept Mech Engn, Fukuoka 8190395, Japan
[2] Kyushu Univ, Int Inst Carbon Neutral Energy Res, Fukuoka 8190395, Japan
关键词
HYDROGELS; FRACTURE;
D O I
10.1103/PhysRevLett.124.068002
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The toughening of sparse elastic networks, such as hydrogels, foams, or meshes against fracture is one of the most important problems in materials science. However, the principles of toughening have not yet been established despite urgent engineering requirements and several efforts made by materials scientists. Here we address the above-mentioned problem by focusing on the topology of a network. We perform fracture experiments for two-dimensional periodic lattices fabricated from rubber strings and connecters with well-defined topological structures. We find that systematic increase in the largest coordination number while maintaining the average coordination number (= 4) as constant leads to significant improvement in toughness. We reproduce the observed toughening behavior through numerical simulations and confirm that the stress concentration in the vicinity of a crack tip can be controlled by the topology of the network. This provides a new strategy for creating tough sparse elastic networks, especially hydrogels.
引用
收藏
页数:5
相关论文
共 50 条
  • [1] SPARSE TOPOLOGY IDENTIFICATION FOR POINT PROCESS NETWORKS
    Pasha, Syed Ahmed
    Solo, Victor
    2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, : 2196 - 2200
  • [2] On a Sparse Shortcut Topology of Artificial Neural Networks
    Fan F.-L.
    Wang D.
    Guo H.
    Zhu Q.
    Yan P.
    Wang G.
    Yu H.
    IEEE Transactions on Artificial Intelligence, 2022, 3 (04): : 595 - 608
  • [3] Optimal sparse network topology under sparse control in Laplacian networks
    Tang, Wentao
    Constantino, Pedro H.
    Daoutidis, Prodromos
    IFAC PAPERSONLINE, 2019, 52 (20): : 273 - 278
  • [4] Topology Identification for Sparse Dynamic Point Process Networks
    Pasha, Syed Ahmed
    Solo, Victor
    2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2014, : 1786 - 1791
  • [5] Topology Identification of Sparse Networks of Continuous Time Systems
    Varanasi, Santhosh Kumar
    Jampana, Phanindra
    2018 INDIAN CONTROL CONFERENCE (ICC), 2018, : 95 - 100
  • [6] Virtual Topology Mapping in Elastic Optical Networks
    Zhao, Juzi
    Subramaniam, Suresh
    Brandt-Pearce, Maite
    2013 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2013, : 3904 - +
  • [7] Estimating topology of complex networks based on sparse Bayesian learning
    Hao Chong-Qing
    Wang Jiang
    Deng Bin
    Wei Xi-Le
    ACTA PHYSICA SINICA, 2012, 61 (14)
  • [8] Topology Effects on Sparse Control of Complex Networks with Laplacian Dynamics
    Pedro H. Constantino
    Wentao Tang
    Prodromos Daoutidis
    Scientific Reports, 9
  • [9] Distributed Topology Identification for Sparse Point Process Dynamic Networks
    Pasha, Syed Ahmed
    Solo, Victor
    2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2015, : 3379 - 3384
  • [10] Topology Effects on Sparse Control of Complex Networks with Laplacian Dynamics
    Constantino, Pedro H.
    Tang, Wentao
    Daoutidis, Prodromos
    SCIENTIFIC REPORTS, 2019, 9 (1)