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Direct numerical simulation of sub-grid structures in gas-solid flow-GPU implementation of macro-scale pseudo-particle modeling

机译:气固两相流-GPU中子网格结构的直接数值模拟实现大规模伪粒子建模

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摘要

Due to significant multi-scale heterogeneity, understanding sub-grid structures is critical to effective continuum-based description of gas-solid flow. However, it is challenging for both physical measurements and numerical simulations. In this article, with the macro-scale pseudo-particle method (MaPPM) implemented on a GPU-based HPC system, up to 30,000 fluidized solids are simulated using the N-S equation directly. The destabilization of uniform suspensions and the formation of solids clusters are reproduced in two-dimensional suspensions. Distinct scale-dependence of the statistical properties in the systems at moderate solid/gas density ratio is observed. Obvious cluster formation and its effect on drag coefficient are shown in a system at high solid/gas density ratio. On the computational side, about 19 folds speedup is obtained on one GT200 GPU, as compared to a mainstream CPU core. The necessity for investigating even larger systems is prospected.
机译:由于存在显着的多尺度异质性,因此了解子网格结构对于有效地基于连续介质描述气固流至关重要。然而,对于物理测量和数值模拟而言都是挑战性的。在本文中,通过在基于GPU的HPC系统上实施的宏观伪粒子方法(MaPPM),可以直接使用N-S方程模拟多达30,000个流化固体。在二维悬浮液中再现了均匀悬浮液的不稳定和固体团簇的形成。在适度的固/气密度比下,观察到系统中统计特性的明显比例依赖性。在高固/气密度比的系统中显示出明显的团簇形成及其对阻力系数的影响。在计算方面,与主流CPU内核相比,一个GT200 GPU可获得约19倍的加速。展望了研究更大系统的必要性。

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