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Using a half-implicit integration scheme for the SPH-based solution of fluid-solid interaction problems

机译:使用半隐式积分方案求解基于SPH的流固耦合问题

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A Smoothed Particles Hydrodynamics (SPH) method for fluid dynamics is coupled with a rigid and deformable body dynamics solution to yield a framework for solving fluid-solid interaction (FSI) problems. The two-way, force-displacement, coupling of the fluid and solid phases is captured via boundary condition enforcing (BCE) markers. The partial differential equations governing each phase are discretized in space separately and the resulting ordinary and/or algebraic differential equations are integrated in time independently with coupling enforced via fluid-solid boundary conditions. Particular attention is paid to enforcing fluid incompressibility via a projection step that computes the pressure field as the solution of a linear system. The numerical solution leverages hybrid parallel computing: the rigid and flexible body dynamics is handled on the CPU using multiple cores; at the same time, the fluid phase is handled on the graphics processing unit (GPU). The methodology is validated against experimental data and numerical results obtained using two open-source solvers. Several case studies are reported to gauge the accuracy, efficiency, and scalability of the solver. The highlights of the proposed solution are: tight enforcement of fluid incompressibility; ability to handle coupled physics that combines fluid, rigid, and deformable bodies; ability to handle friction and contact; and, scalable implementation that simultaneously employs CPU and GPU computing. (C) 2018 Elsevier B.V. All rights reserved.
机译:用于流体动力学的平滑粒子流体动力学(SPH)方法与刚性和可变形的体动力学解决方案相结合,从而为解决流固耦合(FSI)问题提供了框架。液相和固相的双向力-位移-耦合通过边界条件执行(BCE)标记捕获。控制每个相的偏微分方程在空间上分别离散,所得的常微分方程和/或代数微分方程在时间上独立积分,并通过流固边界条件进行耦合。要特别注意通过投影步骤来增强流体的不可压缩性,该步骤将压力场计算为线性系统的解。数值解决方案利用混合并行计算:刚体和柔性体动力学在CPU上使用多个内核进行处理;同时,在图形处理单元(GPU)上处理流体相。该方法论已针对使用两个开源求解器获得的实验数据和数值结果进行了验证。据报道,有几个案例研究用于衡量求解器的准确性,效率和可伸缩性。提出的解决方案的重点是:严格执行流体不可压缩性;处理结合了流体,刚体和可变形体的耦合物理的能力;处理摩擦和接触的能力;以及同时采用CPU和GPU计算的可扩展实现。 (C)2018 Elsevier B.V.保留所有权利。

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