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A Newton-Krylov-FETI-DP Method with an Adaptive Coarse Space Applied to Elastoplasticity

机译:一种牛顿-Krylov-Feti-DP方法,其具有适应弹性塑性的适应性粗糙空间

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We consider a Newton-Krylov-FETI-DP algorithm to solve problems in elastoplasticity. First, the material model and its discretization will be described. The model contains a Prandtl-Reuss flow rule and a von Mises flow function. We restrict ourselves to the case of perfect elastoplasticity; thus, there is no hardening. For more information on elastoplasticity; see, e.g., . In this material model we will have local nonlinearities introduced by plastic material behavior in activated zones of the domain. For the finite element discretization we follow the framework given in. Second, we will briefly present the linearization and the FETI-DP method which is used to solve the linearized problems. For more details on the FETI-DP algorithm, see, e.g., . The convergence of the Newton-Krylov-FETI-DP method using a standard coarse space with vertices and edge averages can deteriorate when the plastically activated zone intersects the interface introduced by the domain decomposition. In this case, we use an adaptive coarse space which successfully decreases the number of cg iterations and the condition numbers of the preconditioned linearized systems. Only a small amount of adaptive constraints is needed if the plastically activated zone is restricted to a small part of the domain. Additional constraints are needed mainly in the final time and Newton steps. The additional constraints for the coarse space are chosen by a strategy proposed in for linear elliptic problems. In contrast to their implementation, here, the additional constraints will be implemented using a deflation approach; see.
机译:我们考虑一种牛顿-Krylov-rei-DP算法来解决弹性塑性的问题。首先,将描述材料模型及其离散化。该模型包含Prandtl-Reuss Flow规则和Von Mises Flow功能。我们将自己限制在完美弹塑性的情况下;因此,没有硬化。有关弹塑性的更多信息;参见,例如,。在该材料模型中,我们将通过域的激活区域中的塑料材料行为引入局部非线性。对于有限元离散化,我们遵循所提供的框架。第二,我们将简要介绍用于解决线性化问题的线性化和Feti-DP方法。有关Feti-DP算法的更多详细信息,请参阅,例如,。当塑性激活的区域与由域分解引入的接口相交时,使用标准粗糙空间的牛顿-Krylov-Feti-DP方法的收敛可能会恶化。在这种情况下,我们使用的自适应粗略空间,该自适应粗略空间可以成功减少CG迭代的数量和预先说明的线性化系统的条件号。如果塑性激活的区域限制在域的一小部分,则只需要少量的自适应约束。需要额外的约束主要是在最后一次和牛顿步骤中。由用于线性椭圆问题所提出的策略选择粗糙空间的附加约束。与他们的实现相比,这里,将使用通货紧缩方法实现附加约束;看。

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