Computational modeling and validation of human nasal airflow under various breathing conditions.
Computational modeling and validation of human nasal airflow under various breathing conditions.
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DOI：
10.1016/j.jbiomech.2017.08.031
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发表时间：
2017-11-07
影响因子：
2.4
通讯作者：
Zhao K
中科院分区：
文献类型：
--
作者：
Li C;Jiang J;Dong H;Zhao K
人的鼻子具有重要的生理功能，包括加热、过滤、加湿和嗅觉。这些功能是基于依赖于鼻腔气流模式和湍流的运输现象。准确预测这些气流特性需要仔细选择计算流体动力学模型和严格的验证。过去的验证研究受到复杂的鼻腔几何形状的不良表征，缺乏详细的气流比较以及流量范围的限制。本研究的目的是在呼吸流量为180至1100ml /s的情况下，根据已发表的实验测量数据，验证基于解剖学精确的鼻模型的各种数值方法。采用层流模型、四种广泛使用的Reynolds-average Navier-Stokes （RANS）湍流模型（即k-、标准k-剪切应力传输k-和Reynolds应力模型）、大涡模拟（LES）模型和直接数值模拟（DNS）模型获得了速度剖面和湍流强度的数值结果。结果发现，在静息呼吸条件下（180 ml/s），层流模型与实验结果吻合较好，流场存在一定的不均匀性，且优于RANS模型。随着呼吸流量的增加，RANS模型获得了更准确的预测，但仍然比LES和DNS表现差。正如预期的那样，LES和DNS可以提供所有流动条件下鼻腔气流的准确预测，但计算成本高出约100倍。在所有测试的RANS模型中，标准k-模型在速度分布和湍流强度方面与实验值最接近。
The human nose serves vital physiological functions, including warming, filtration, humidification, and olfaction. These functions are based on transport phenomena that depend on nasal airflow patterns and turbulence. Accurate prediction of these airflow properties requires careful selection of computational fluid dynamics models and rigorous validation. The validation studies in the past have been limited by poor representations of the complex nasal geometry, lack of detailed airflow comparisons, and restricted ranges of flow rate. The objective of this study is to validate various numerical methods based on an anatomically accurate nasal model against published experimentally measured data under breathing flow rates from 180 to 1100 ml/s. The numerical results of velocity profiles and turbulence intensities were obtained using the laminar model, four widely used Reynolds-averaged Navier-Stokes (RANS) turbulence models (i.e., k- , standard k- Shear Stress Transport k- , and Reynolds Stress Model), large eddy simulation (LES) model, and direct numerical simulation (DNS). It was found that, despite certain irregularity in the flow field, the laminar model achieved good agreement with experimental results under restful breathing condition (180 ml/s) and performed better than the RANS models. As the breathing flow rate increased, the RANS models achieved more accurate predictions but still performed worse than LES and DNS. As expected, LES and DNS can provide accurate predictions of the nasal airflow under all flow conditions but have an approximately 100-fold higher computational cost. Among all the RANS models tested, the standard k- model agrees most closely with the experimental values in terms of velocity profile and turbulence intensity.
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DOI：
10.1590/1414-431x20165182
发表时间：
2016-08-01
期刊：
Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas
影响因子：
--
作者：
Wang T;Chen D;Wang PH;Chen J;Deng J
通讯作者：
Deng J
影响因子：
3.8
作者：
Croce, Celine;Fodil, Redouane;Louis, Bruno
通讯作者：
Louis, Bruno
DOI：
10.1243/09544119jeim330
发表时间：
2008-05-01
影响因子：
1.8
作者：
Doorly, D.;Taylor, D. J.;Schroter, R. C.
通讯作者：
Schroter, R. C.
影响因子：
3.3
作者：
Kelly, JT;Prasad, AK;Wexler, AS
通讯作者：
Wexler, AS
影响因子：
3.6
作者：
Li, Chengyu;Dong, Haibo;Liu, Geng
通讯作者：
Liu, Geng