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.

中文翻译：

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使用两方程湍流模型模拟类似于上气道条件下微粒沉积的建议

CFD 模型的开发，从初始几何到具有工程洞察力的实验验证结果，可能是一个耗时的过程，通常需要多次迭代网格划分和求解器设置。在早期阶段应用一套指南有助于简化流程并提高不同模型之间的一致性。本研究的目的是确定网格和 CFD 求解参数，以便在与上呼吸道（包括湍流）一致的流动条件下准确模拟微粒沉积。90° 弯曲几何形状用作整个气道中出现的特征模型，并且在过渡和湍流流态中可以获得高质量的实验气溶胶沉积数据。对具有不同近壁分辨率的四种网格进行了比较，并应用了关键求解器设置来确定使对近壁 (NW) 网格敏感度最小的参数。低雷诺数 (LRN) k-ω 模型用于解析湍流场，这是一种数值上有效的两方程湍流模型，但最近被认为过于简单化。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)，来克服二方程模型的弱点。因此，次要目标是确定计算效率更高的 LRN k-ω 模型是否能够提供与 LES 相当的沉积结果。结果显示了如何通过应用基于 Green-Gauss 节点的梯度离散化方案和物理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解决方案指南可与实验数据完美匹配。此外，来自 LRN k-ω 模型的沉积数据与相同特征几何形状的 LES 结果相比更有利。总之，本研究提供了一组网格划分和求解指南，用于使用数值有效的 LRN k-ω 方法模拟上呼吸道中发现的过渡和湍流中的气溶胶沉积。来自 LRN k-ω 模型的沉积数据与相同特征几何形状的 LES 结果相比具有优势。总之，本研究提供了一组网格划分和求解指南，用于使用数值有效的 LRN k-ω 方法模拟上呼吸道中发现的过渡和湍流中的气溶胶沉积。来自 LRN k-ω 模型的沉积数据与相同特征几何形状的 LES 结果相比具有优势。总之，本研究提供了一组网格划分和求解指南，用于使用数值有效的 LRN k-ω 方法模拟上呼吸道中发现的过渡和湍流中的气溶胶沉积。