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Augmented Lagrange methods for quasi-incompressible materials-Applications to soft biological tissue

机译:准不可压缩材料的增强拉格朗日方法-在软生物组织中的应用

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Arterial walls in the healthy physiological regime are characterized by quasi-incompressible, anisotropic, hyperelastic material behavior. Polyconvex material functions representing such materials typically incorporate a penalty function to account for the incompressibility. Unfortunately, the penalty will affect the conditioning of the stiffness matrices. For high penalty parameters, the performance of iterative solvers will degrade, and when direct solvers are used, the quality of the solutions will deteriorate. In this paper, an augmented Lagrange approach is used to cope with the quasi-incompressibility condition. Here, the penalty parameter can be chosen much smaller, and as a consequence, the arising linear systems of equations have better properties. An improved convergence is then observed for the finite element tearing and interconnecting-dual primal domain decomposition method, which is used as an iterative solver. Numerical results for an arterial geometry obtained from ultrasound imaging are presented.
机译:在健康的生理状态下,动脉壁的特征是准不可压缩的,各向异性的,超弹性的材料行为。代表这种材料的多凸材料功能通常包含惩罚功能以解决不可压缩性。不幸的是,代价将影响刚度矩阵的调节。对于高罚分参数,迭代求解器的性能将下降,而使用直接求解器时,求解器的质量将下降。在本文中,使用增强的拉格朗日方法来解决准不可压缩条件。在此,惩罚参数可以选择得小得多,因此,所产生的线性方程组具有更好的特性。然后观察到有限元撕裂和互连对偶原始域分解方法的改进收敛性,该方法用作迭代求解器。给出了从超声成像获得的动脉几何形状的数值结果。

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