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A parallel sparse algorithm targeting arterial fluid mechanics computations

机译:针对动脉流体力学计算的并行稀疏算法

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Iterative solution of large sparse nonsymmetric linear equation systems is one of the numerical challenges in arterial fluid–structure interaction computations. This is because the fluid mechanics parts of the fluid + structure block of the equation system that needs to be solved at every nonlinear iteration of each time step corresponds to incompressible flow, the computational domains include slender parts, and accurate wall shear stress calculations require boundary layer mesh refinement near the arterial walls. We propose a hybrid parallel sparse algorithm, domain-decomposing parallel solver (DDPS), to address this challenge. As the test case, we use a fluid mechanics equation system generated by starting with an arterial shape and flow field coming from an FSI computation and performing two time steps of fluid mechanics computation with a prescribed arterial shape change, also coming from the FSI computation. We show how the DDPS algorithm performs in solving the equation system and demonstrate the scalability of the algorithm.
机译:大型稀疏非对称线性方程组的迭代求解是动脉流-结构相互作用计算中的数值挑战之一。这是因为方程式系统的流体+结构块的流体力学部分需要在每个时间步长的每个非线性迭代中进行求解,这对应于不可压缩的流动,计算域包括细长部分,并且精确的壁面剪应力计算需要边界动脉壁附近的多层网格细化。我们提出了一种混合并行稀疏算法,即域分解并行求解器(DDPS),以应对这一挑战。作为测试用例,我们使用了流体力学方程组,该系统是通过从FSI计算得出的动脉形状和流场开始,并从FSI计算得出的具有规定的动脉形状变化的流体力学计算的两个时间步中生成的。我们展示了DDPS算法在求解方程组中的性能,并展示了该算法的可扩展性。

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