Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: Current Applications and Prospects

O. Ayodele, Atoyebi Ebenezer Oluwatosin, Olutosoye Christian Taiwo, A. Dare
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引用次数: 1

Abstract

Breathing conditions pertaining to nasal obstruction, obstructive sleep apnea, and airflow resistance in the human lower airways have been investigated extensively by researchers over the years. Due to the availability of advanced computer numerical models, such as computational fluid dynamics (CFD), researchers have made progressive studies of airflow characteristic, especially the effects of airflow pressure, velocity and wall shear stress in human obstructive airways. Studies utilizing CFD have enhanced clinical understanding of the physiology and pathophysiology of the respiratory system through the concept of three-dimensional models that facilitate airflow simulation. The main objective of this article is to review recent CFD literature on nasal airflow and lower airway obstruction. The review covers the role of segmentation threshold in the outcome of airflow simulation in the nasal cavity, and results of fluid structure interaction (FSI) and computational fluid dynamics in nasal obstruction and airway collapse in obstructive sleep apnea were also correlated. For models of the lower airways, we evaluated the effect of extra-thoracic airway (ETA) on downstream airflow during simulation against the popular Weibel’s model. In the concluding section, we discussed the advantages, limitations, and prospects (precisely with deep machine learning) of computational fluid dynamics in the clinical assessment and investigation of respiratory diseases.
计算流体动力学建模在呼吸道阻塞中的应用现状与展望
多年来,研究人员对人类下气道中与鼻塞、阻塞性睡眠呼吸暂停和气流阻力有关的呼吸条件进行了广泛的研究。由于计算流体动力学(CFD)等先进的计算机数值模型的可用性,研究人员对气流特性进行了深入的研究,特别是气流压力、速度和壁面剪切应力对人体阻塞性气道的影响。利用CFD的研究通过促进气流模拟的三维模型概念,增强了对呼吸系统生理和病理生理的临床理解。本文的主要目的是回顾最近关于鼻腔气流和下气道阻塞的CFD文献。本文综述了分割阈值在鼻腔气流模拟结果中的作用,以及阻塞性睡眠呼吸暂停中鼻塞和气道塌陷的流体结构相互作用(FSI)和计算流体动力学结果的相关性。对于下气道模型,我们根据流行的Weibel模型在模拟过程中评估了胸外气道(ETA)对下游气流的影响。在结论部分,我们讨论了计算流体动力学在呼吸系统疾病的临床评估和研究中的优势、局限性和前景(精确地与深度机器学习一起)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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