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Recommendations for Simulating Microparticle Deposition at Conditions Similar to the Upper Airways with Two-Equation Turbulence Models

机译:关于采用两方程湍流模型模拟与上层气道相似的条件下微粒沉积的建议

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摘要

The development of a CFD model, from initial geometry to experimentally validated result with engineering insight, can be a time-consuming process that often requires several iterations of meshing and solver set-up. Applying a set of guidelines in the early stages can help to streamline the process and improve consistency between different models. The objective of this study was to determine both mesh and CFD solution parameters that enable the accurate simulation of microparticle deposition under flow conditions consistent with the upper respiratory airways including turbulent flow. A 90° bend geometry was used as a characteristic model that occurs throughout the airways and for which high-quality experimental aerosol deposition data is available in the transitional and turbulent flow regimes. Four meshes with varying degrees of near-wall resolution were compared, and key solver settings were applied to determine the parameters that minimize sensitivity to the near-wall (NW) mesh. The Low Reynolds number (LRN) k-ω model was used to resolve the turbulence field, which is a numerically efficient two-equation turbulence model, but has recently been considered overly simplistic. Some recent studies have used more complex turbulence models, such as Large Eddy Simulation (LES), to overcome the perceived weaknesses of two-equation models. Therefore, the secondary objective was to determine whether the more computationally efficient LRN k-ω model was capable of providing deposition results that were comparable to LES. Results show how NW mesh sensitivity is reduced through application of the Green-Gauss Node-based gradient discretization scheme and physically realistic near-wall corrections. Using the newly recommended meshing parameters and solution guidelines gives an excellent match to experimental data. Furthermore, deposition data from the LRN k-ω model compares favorably with LES results for the same characteristic geometry. In summary, this study provides a set of meshing and solution guidelines for simulating aerosol deposition in transitional and turbulent flows found in the upper respiratory airways using the numerically efficient LRN k-ω approach.
机译:从初始几何图形到具有工程洞察力的经实验验证的结果,CFD模型的开发可能是一个耗时的过程,通常需要进行多次网格划分和求解器设置。在早期阶段应用一组准则可以帮助简化流程并提高不同模型之间的一致性。这项研究的目的是确定网格和CFD解决方案参数,从而能够在与上呼吸道(包括湍流)一致的流动条件下准确模拟微粒沉积。 90°弯曲的几何形状被用作整个气道的特征模型,并且在过渡和湍流状态下可获得高质量的实验气溶胶沉积数据。比较了具有不同程度的近壁分辨率的四个网格,并应用了关键求解器设置来确定使对近壁(NW)网格的敏感性最小的参数。低雷诺数(LRN)k-ω模型用于求解湍流场,该场是数值有效的两方程湍流模型,但最近被认为过于简单。最近的一些研究使用了更复杂的湍流模型,例如大涡模拟(LES),以克服人们认为的两方程模型的弱点。因此,第二个目的是确定计算效率更高的LRNk-ω模型是否能够提供与LES相当的沉积结果。结果表明,如何通过应用基于Green-Gauss Node的梯度离散方案和物理上逼真的近壁校正来降低NW网格灵敏度。使用新推荐的网格划分参数和求解准则可以很好地匹配实验数据。此外,对于相同的特征几何,来自LRNk-ω模型的沉积数据可与LES结果进行比较。总而言之,这项研究提供了一套网格划分和求解准则,以使用数值有效的LRNk-ω方法模拟在上呼吸道中发现的过渡流和湍流中的气溶胶沉积。

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