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Multiscale model of fatigue of collagen gels

机译:胶原凝胶疲劳的多尺度模型

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Fatigue as a mode of failure becomes increasingly relevant with age in tissues that experience repeated fluctuations in loading. While there has been a growing focus on the mechanics of networks of collagen fibers, which are recognized as the predominant mechanical components of soft tissues, the network's fatigue behavior has received less attention. Specifically, it must be asked (1) how the fatigue of networks differs from that of its component fibers, and (2) whether this difference in fatigue behaviors is affected by changes in the network's architecture. In the present study, we simulated cyclic uniaxial loading of Voronoi networks to model fatigue experiments performed on reconstituted collagen gels. Collagen gels were cast into dog-bone shape molds and were tested on a uniaxial machine under a tension-tension cyclic loading protocol. Simulations were performed on networks modeled as trusses of, on average, 600 nonlinear elastic fibers connected at freely rotating pin-joints. We also simulated fatigue failure of Delaunay, and Erds-Renyi networks, in addition to Voronoi networks, to compare fatigue behavior among different architectures. The uneven distribution of stresses within the fibers of the unstructured networks resulted in all three network geometries being more endurant than a single fiber or a regular lattice under cyclic loading. Among the different network geometries, for low to moderate external loads, the Delaunay networks showed the best fatigue behavior, while at higher loads, the Voronoi networks performed better.
机译:随着失败模式的疲劳与经历重复的装载波动的年龄越来越相关。虽然较大的胶原纤维网络机械师越来越焦点,但被认为是软组织的主要机械成分,但网络的疲劳行为受到不太关注。具体来说,必须询问(1)网络的疲劳如何与其组成纤维的疲劳程度不同,并且(2)疲劳行为的这种差异是否受网络架构的变化影响。在本研究中,我们模拟了Voronoi网络的循环单轴载荷,以模拟对重构胶原凝胶进行的疲劳实验。将胶原凝胶铸造成狗骨形状模具,并在张紧张力环状加载方案下在单轴机上进行测试。在以自由旋转销接头连接的平均600个非线性弹性纤维建模的网络上进行模拟。除了Voronoi网络之外,我们还模拟了Delaunay和Erds-Renyi网络的疲劳失败,以比较不同架构中的疲劳行为。非结构化网络的纤维内应力的不均匀分布导致所有三个网络几何形状比单纤维更耐凝固,或在循环载荷下的常规格子。在不同的网络几何形状中,对于低至中等的外部负载,Delaunay网络显示了最佳的疲劳行为,而在较高的负载时,Voronoi网络更好地执行。

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