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Visco-hyperelastic law for finite deformations: A frequency analysis

机译:有限变形的粘超弹性定律:频率分析

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摘要

Some biological tissues are repeatedly stimulated under cyclic loading, and this stimulation can be combined with large pressures, thus leading to large deformations. For such applications, visco-hyperelastic models have been proposed in the literature and used in finite-element studies. An extensively used quasi-linear model (QLVH), which assumes linear evolution equations, is compared with a nonlinear model (NLVH), which assumes a multiplicative split of the deformation gradient. The comparison is made here using sets of simulations covering a large frequency range. Lost and stored energies are computed, and the additional parameter of the NLVH model is set to two values found in the literature (NLVH-2 and NLVH-30 models). The predicted behaviour is very similar for all models at small strains, with each time constant (and corresponding viscous modulus) being associated with a damping peak and a stored-energy increase. When the strain amplitude is increased, the ratio of lost to stored energy increases for the QLVH model, but decreases for the NLVH models. The NLVH-30 model also displays a shift of the peak damping towards higher frequencies. Before reaching a steady state, all models display a decay of energy independent of the frequency, and the additional parameter of the NLVH model permits the modelling of complex types of evolution of the damping. In conclusion, this study compares the behaviour of two viscous hyper-elastic laws to allow an informed choice between them.
机译:一些生物组织在循环载荷下反复受到刺激,并且这种刺激可以与大压力结合在一起,从而导致大的变形。对于这种应用,已经在文献中提出了粘-超弹性模型并将其用于有限元研究中。将假设线性演化方程的广泛使用的准线性模型(QLVH)与假定变形梯度为乘法的非线性模型(NLVH)进行比较。这里使用覆盖较大频率范围的一组模拟进行比较。计算损失的能量和存储的能量,并将NLVH模型的附加参数设置为文献中找到的两个值(NLVH-2和NLVH-30模型)。所有模型在小应变下的预测行为都非常相似,每个时间常数(和相应的粘性模量)都与阻尼峰值和储能增加相关。当应变幅度增加时,对于QLVH模型,损失的能量与存储的能量的比率增加,但对于NLVH模型,损失的能量与存储的能量的比率减小。 NLVH-30模型还显示了峰值阻尼向更高频率的偏移。在达到稳态之前,所有模型都显示出与频率无关的能量衰减,并且NLVH模型的附加参数允许对复杂的阻尼演化类型进行建模。总之,本研究比较了两个粘性超弹性定律的行为,以便在它们之间进行明智的选择。

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