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A CPU-GPU cross-platform coupled CFD-DEM approach for complex particle-fluid flows

机译:一种CPU-GPU跨平台耦合CFD-DEM方法,用于复杂颗粒流体流动

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High computational cost presents a significant barrier to the general application of coupled computational fluid dynamics and discrete element method (CFD-DEM) simulations, especially so for industrial systems with a large number of particles and complex geometries. In this study, a new cross-platform coupling approach is developed by integrating a CFD solver with a standalone GPU-based DEM solver via network communication. Consequently, the two modelling techniques benefit from the most appropriate hardware architecture. The developed coupling approach shows predictions comparable to experiments on a small-scale fluidized bed. Its computational performance is evaluated on a larger fluidized bed and shows superior performance over the CPU-based parallelization methods, making DEM calculation no longer the computational bottleneck. Its general applicability to handle complex geometrical domains is further demonstrated by simulations of a gas-solid cyclone separator. This work demonstrates the benefits of a novel coupling approach which enables efficient and robust solutions for industrial applications. (C) 2020 Elsevier Ltd. All rights reserved.
机译:高计算成本对耦合计算流体动力学和离散元件方法(CFD-DEM)模拟的一般应用具有显着障碍,特别是对于具有大量粒子和复杂几何形状的工业系统。在该研究中,通过通过网络通信集成具有基于独立的GPU的DEM解器的CFD求解器来开发新的跨平台耦合方法。因此,两个建模技术受益于最合适的硬件架构。开发的耦合方法显示了与小规模流化床上的实验相当的预测。其计算性能在更大的流化床上进行评估,并在基于CPU的并行化方法上显示出优异的性能,使DEM计算不再是计算瓶颈。通过模拟气体固体旋风分离器的模拟进一步证明了其对处理复杂几何结构域的一般适用性。这项工作展示了一种新颖耦合方法的优势,这使得工业应用的高效和强大的解决方案。 (c)2020 elestvier有限公司保留所有权利。

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