多因素影响下的人体肺腺泡区吸入颗粒物沉积规律数值模拟研究

Numerical simulation on the deposition characteristics of inhaled particles in human pulmonary acinus region under the influence of multi-factors

研究颗粒物在人体肺腺泡区的沉积规律对了解肺部疾病致病机制、促进肺部疾病的预防和治疗都有重要意义。现行研究大多关注颗粒物在人体肺腺泡区的最终沉积率,而对其沉积动态过程鲜有涉及。本文建立了G3~G7共五个多肺泡模型,引入颗粒物沉积速度评价参数1/4沉积时间,通过数值模拟研究了模型级数和结构、颗粒物粒径、呼吸模式等因素对0.1~5μm粒径范围内颗粒物沉积规律的影响,归纳总结了不同粒径颗粒物的沉积特点,为进一步了解人体肺腺泡区颗粒物的运动规律提供了新视角。结果表明:模型级数和结构是影响各粒径颗粒物沉积的重要因素。0.1μm颗粒物沉积受布朗力主导,沉积率高,沉积速度快,沉积曲线呈对数型分布;5μm颗粒物沉积受重力主导,沉积率高,沉积较快,沉积曲线呈“S”型分布;0.3~1μm颗粒物沉积则受惯性冲击影响较大,随着呼吸模式的改变沉积规律变化明显。本文的研究方法和结果能为进一步探究肺部疾病致病机制和肺部疾病的预防与治疗提供理论依据和数据支持。

Research on the deposition of inhaled particles in human pulmonary acinus region is important to the pathogenesis investigation,prevention and treatment of lung diseases.Most of the current research focus on the final deposition fraction of inhaled particles in human acinar region,but little is involved in their dynamic deposition characteristics.In this paper,five multi-alveolar models,G3−G7,were built.The evaluation parameter 1/4 deposition time was introduced to study the particle deposition speed.The deposition characteristics of particles in the diameter ranging 0.1−5μm were numerically simulated and summarized under the influence of factors such as the generation and structure of model,particle diameter and respiratory mode,shedding some new light on the further research of transport of inhaled particles.The results showed that the generation and structure of model had a significance effect on the deposition of particles.0.1μm particles were dominated by Brownian diffusion,which experienced a high deposition fraction,a fast deposition speed and a logarithmic deposition curve,while 5μm particles were dominated by gravitational sedimentation,with a high deposition fraction,a fast deposition speed and an S-shaped deposition curve.The deposition of 0.3−1μm particles were influenced greatly by convention and varied with the change of respiratory mode.The research methods and results in this paper can provide theoretical basis and data support for the further exploration of the mechanism,prevention and treatment of lung diseases.