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首页> 外文期刊>Journal of Aerosol Science >Three-dimensional computational fluid dynamics modeling of particle uptake by an occupational air sampler using manually-scaled and adaptive grids
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Three-dimensional computational fluid dynamics modeling of particle uptake by an occupational air sampler using manually-scaled and adaptive grids

机译:使用手动缩放和自适应网格的职业空气采样器对颗粒吸收的三维计算流体动力学建模

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This work presents fluid flow and particle trajectory simulation studies to determine the aspiration efficiency of a horizontally oriented occupational air sampler using computational fluid dynamics (CFD). Grid adaption and manual scaling of the grids were applied to two sampler prototypes based on a 37-mm cassette. The standard k-epsilon model was used to simulate the turbulent air flow and a second order streamline-upwind discretization scheme was used to stabilize convective terms of the Navier-Stokes equations. Successively scaled grids for each configuration were created manually and by means of grid adaption using the velocity gradient in the main flow direction. Solutions were verified to assess iterative convergence, grid independence and monotonic convergence. Particle aspiration efficiencies determined for both prototype samplers were undistinguishable, indicating that the porous filter does not play a noticeable role in particle aspiration. Results conclude that grid adaption is a powerful tool that allows to refine specific regions that require lots of detail and therefore better resolve flow detail. It was verified that adaptive grids provided a higher number of locations with monotonic convergence than the manual grids and required the least computational effort. (C) 2016 Elsevier Ltd. All rights reserved.
机译:这项工作提出了流体流动和粒子轨迹模拟研究,以确定使用计算流体力学(CFD)的水平定向职业空气采样器的抽吸效率。网格调整和网格的手动缩放应用于基于37毫米卡带的两个采样器原型。使用标准的kε模型来模拟湍流,并使用二阶流线-上风离散化方案来稳定Navier-Stokes方程的对流项。手动创建每种配置的连续缩放网格,并使用主流方向上的速度梯度通过网格自适应来创建。验证了解决方案以评估迭代收敛,网格独立性和单调收敛。对于两个原型采样器确定的颗粒抽吸效率是无法区分的,这表明多孔过滤器在颗粒抽吸中没有明显的作用。结果得出结论,网格自适应是一种强大的工具,可用于优化需要大量细节的特定区域,从而更好地解决流细节。事实证明,自适应网格提供的位置具有比手动网格更多的单调收敛性,并且所需的计算量最少。 (C)2016 Elsevier Ltd.保留所有权利。

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