期刊
出版社
WILEY
DOI: 10.1002/cnm.2948
关键词
augmented Lagrangian; cardiac mechanics; preconditioning techniques
类别
资金
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior
- Financiadora de Estudos e Projetos
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
- Fundacao de Amparo a Pesquisa do Estado de Minas Gerais [APQ-02537-15]
- Universidad de Federal de Juiz de Fora
- Research Council of Norway
Computational modeling of the heart is a subject of substantial medical and scientific interest, which may contribute to increase the understanding of several phenomena associated with cardiac physiological and pathological states. Modeling the mechanics of the heart have led to considerable insights, but it still represents a complex and a demanding computational problem, especially in a strongly coupled electromechanical setting. Passive cardiac tissue is commonly modeled as hyperelastic and is characterized by quasi-incompressible, orthotropic, and nonlinear material behavior. These factors are known to be very challenging for the numerical solution of the model. The near-incompressibility is known to cause numerical issues such as the well-known locking phenomenon and ill-conditioning of the stiffness matrix. In this work, the augmented Lagrangian method is used to handle the nearly incompressible condition. This approach can potentially improve computational performance by reducing the condition number of the stiffness matrix and thereby improving the convergence of iterative solvers. We also improve the performance of iterative solvers by the use of an algebraic multigrid preconditioner. Numerical results of the augmented Lagrangian method combined with a preconditioned iterative solver for a cardiac mechanics benchmark suite are presented to show its improved performance.
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