论文标题
在中等院长数量的弯曲微流体管道中球形颗粒的惯性聚焦
Inertial focusing of spherical particles in curved microfluidic ducts at moderate Dean numbers
论文作者
论文摘要
我们检查了院长数对悬浮在弯曲的微流体管道流中的球形颗粒的惯性焦点的影响。弯曲导管中粒子迁移的先前建模假设流速足够小,以至于背景流相对于院长数的领先顺序近似产生合理的模型。在此,我们将模型扩展到具有中等院长数(在微流体环境中)的情况,而粒子雷诺数数则保持较小。这一扩展使我们能够捕获随着流速增加而发生的背景流的变化,即当地极端向外墙转移。背景流的轴向速度曲线的变化对惯性提升力有影响,而横截面组件的变化直接影响二次流阻力。为了保持粒子雷诺数少,我们以与先前研究相似的方式近似惯性升力力,同时捕获了微妙的效果对修改的背景流谱。捕获和理解这些效果是迈向准确建模跨多种实际应用的惯性迁移的重要一步。我们的结果揭示了不断变化的背景流图如何改变颗粒的惯性聚焦。我们说明在许多情况下按大小增强了粒子的横向分离,发现聚焦时间可以大致分为两个方案。这些结果表明,我们的模型可能有助于参数选择,以按大小分离粒子。
We examine the effect of Dean number on the inertial focusing of spherical particles suspended in flow through curved microfluidic ducts. Previous modelling of particle migration in curved ducts assumed the flow rate was small enough that a leading order approximation of the background flow with respect to the Dean number produces a reasonable model. Herein, we extend our model to situations having a moderate Dean number (in the microfluidics context) while the particle Reynolds number remains small. This extension allows us to capture changes in the background flow that occur with increasing flow rate, namely a shift in local extrema towards the outside wall. The change in the axial velocity profile of the background flow has an effect on the inertial lift force, while the change in the cross-sectional components directly affects the secondary flow drag. In keeping the particle Reynolds number small we approximate the inertial lift force in a similar manner to previous studies while capturing subtle effects do to the modified background flow profile. Capturing and understanding these effects is an important step towards accurately modelling inertial migration across a wide range of practical applications. Our results reveal how the changing background flow profile modifies the inertial focusing of particles. We illustrate enhanced lateral separation of particles by size in a number of scenarios and find that focusing times can be roughly separated into two regimes. These results suggest our model might aid with parameter choices for separation of particles by size.