Lagrangian multiscale simulation of complex flows
Lagrangian multiscale simulation of complex flows
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DOI：
10.1063/5.0063059
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发表时间：
2021-09
影响因子：
4.6
通讯作者：
Yohei Morii;T. Kawakatsu
中科院分区：
文献类型：
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作者：
Yohei Morii;T. Kawakatsu
本文提出了一个通用的多尺度多物理场非均匀粘弹性和弹塑性复杂流动的模拟框架，如纳米气泡流动、血管流动或聚合物复合流动，可在大规模并行计算机上使用。我们的模拟方法是基于宏观流动的粒子模拟，其中微观模拟器嵌入在宏观流动模拟的每个流体动力学粒子中，以从微观角度评估局部应力作为其流动历史的函数。将光滑粒子流体动力学（SPH）方法与微观分子模拟器相结合，开发了复杂流动多尺度模拟平台MSSP。在这种技术中，主要的困难是由于大量的微观粒子（通常为109 - 1010的数量级）和流动的不均匀性而导致的大量计算成本。为了解决这个问题，我们提出了一个动态切换的微观模型之间的现实粒子模拟和线性本构关系。在SPH模拟中还引入了一个适当的移动边界条件，使我们能够模拟复杂的流动与可变形的物体，如相分离的域或生物膜。通过模拟非线性非马尔可夫流体通过障碍物的情况，对MSSP进行了基准测试，在相同情况下，与实验结果有很好的定量一致性。
A general multiscale and multiphysics simulation framework for inhomogeneous viscoelastic and elastoplastic complex flows, such as nanobubble flows, blood vessel flows, or polymer composite flows, is presented for use on massive parallel computers. Our simulation methodology is based on a particle simulation of macroscopic flows where a microscopic simulator is embedded in each of the hydrodynamic particles of macroscopic flow simulations to evaluate the local stress as a function of its flow history from the microscopic point of view. We developed a platform named MSSP (MultiScale Simulation Platform for complex flows) by combining the smoothed particle hydrodynamics (SPH) method and the microscopic molecular simulators. In such a technique, the main difficulty is the large amount of computation cost due to a large number of microscopic particles (typically of the order of 109−1010), and the inhomogeneity of the flow. To solve this problem, we propose a dynamical switching of the microscopic models between realistic particle simulations and linearized constitutive relations. An appropriate boundary condition for moving boundaries is also introduced in the SPH simulations that enables us to simulate complex flows with deformable objects such as phase-separated domains or biomembranes. A benchmark test of MSSP has been done by simulating nonlinear and non-Markovian fluids passing by an obstacle, giving good quantitative agreement with experiments in the same situation.