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Parameter estimates for the Relaxed Dimensional Factorization preconditioner and application to hemodynamics

机译:松弛维分解因子预处理器的参数估计及其在血液动力学中的应用

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We present new results on the Relaxed Dimensional Factorization (RDF) preconditioner for solving saddle point problems from incompressible flow simulations, first introduced in Benzi et al. (2011). This method contains a parameter alpha > 0, to be chosen by the user. Previous works provided an estimate of a in the 2D case using Local Fourier Analysis. A novel algebraic estimation technique for finding a suitable value of the RDF parameter in both the 2D and the 3D case with arbitrary geometries is proposed. This technique is tested on a variety of discrete saddle point problems arising from the approximation of the Navier-Stokes equations using a Marker-and-Cell scheme and a finite element one. We also show results for a large-scale problem relevant for hemodynamics simulation that we solve in parallel using up to 8196 cores. (C) 2015 Elsevier B.V. All rights reserved.
机译:我们介绍了关于松弛维分解(RDF)预处理器的新结果,该条件用于解决不可压缩流模拟中的鞍点问题,这在Benzi等人中首次引入。 (2011)。此方法包含一个参数alpha> 0,由用户选择。以前的工作使用局部傅立叶分析提供了2D情况下a的估计。提出了一种新颖的代数估计技术,可以在任意几何形状的2D和3D情况下找到合适的RDF参数值。使用Marker-and-Cell方案和有限元方法对由Navier-Stokes方程逼近而产生的各种离散鞍点问题进行了测试。我们还显示了与血液动力学仿真相关的大规模问题的结果,我们最多使用8196个核并行解决了这一问题。 (C)2015 Elsevier B.V.保留所有权利。

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