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EMMS-based discrete particle method (EMMS-DPM) for simulation of gas-solid flows

机译:基于EMMS的离散粒子方法(EMMS-DPM)用于模拟气固流动

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Understanding the hydrodynamics of gas-solid flows is a grand challenge in mechanical and chemical engineering. The continuum-based two-fluid models (TFM) are currently not accurate enough to describe the multi-scale heterogeneity, while the discrete particle method (DPM) following the trajectory of each particle is computationally infeasible for industrial systems. Following our previous work, we report in this article a coarse-grained DPM considering the meso-scale structure based on the energy-minimization multi-scale (EMMS) model, which can be orders of magnitude faster than the traditional DPM and can take full advantage of CPU-CPU (graphics processing unit) hybrid super-computing. The size and solids concentration of the coarse-grained particles (CGP), as well as their interactions with the gas Row (the drag) are determined by the EMMS model with a two-phase decomposition. The interactions between CGPs are determined according to the kinetic theory of granular flows (KTGF). The method is tested by simulating the onset of fluidization and the steady state flow in lab-scale circulating fluidized bed (CFB) risers with different geometries and operating conditions both in 2D and 3D. The results agree well with experiments and traditional DPM based on single particles. The prospect of this method as a higher-resolution alternative to TEM for engineering applications and even for virtual process engineering is discussed finally. (C) 2014 Elsevier Ltd. All rights reserved
机译:在机械和化学工程中,了解气固流动的流体动力学是一个巨大的挑战。当前,基于连续体的双流体模型(TFM)不够精确,无法描述多尺度异质性,而遵循每个粒子轨迹的离散粒子方法(DPM)对于工业系统而言在计算上是不可行的。继我们之前的工作之后,我们在本文中报告了一种基于能量最小化多尺度(EMMS)模型的中尺度结构的粗粒度DPM,它可以比传统DPM快几个数量级并且可以完全CPU-CPU(图形处理单元)混合超级计算的优势。粗粒颗粒(CGP)的大小和固体浓度,以及它们与气体行(阻力)的相互作用是通过EMMS模型进行两相分解来确定的。 CGP之间的相互作用是根据颗粒流动力学理论(KTGF)确定的。通过模拟在2D和3D中具有不同几何形状和操作条件的实验室规模的循环流化床(CFB)立管中的流化过程和稳态流,对该方法进行了测试。结果与基于单个粒子的实验和传统DPM吻合得很好。最后讨论了该方法作为TEM的更高分辨率替代品的前景,以用于工程应用,甚至用于虚拟过程工程。 (C)2014 Elsevier Ltd.保留所有权利

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