A combined Lattice Boltzmann and Immersed boundary approach for predicting the vascular transport of differently shaped particles

格子Boltzmann方法 层流 库埃特流 雷诺数 边值问题 物理 机械 角速度 玻尔兹曼方程 经典力学 流量(数学) 数学 数学分析 湍流 量子力学
作者
Alessandro Coclite,Marco D. de Tullio,G. Pascazio,Paolo Decuzzi
出处
期刊:Computers & Fluids [Elsevier BV]
卷期号:136: 260-271 被引量:28
标识
DOI:10.1016/j.compfluid.2016.06.014
摘要

Modelling the vascular transport and adhesion of man-made particles is crucial for optimizing their efficacy in the detection and treatment of diseases. Here, a Lattice Boltzmann and Immersed Boundary methods are combined together for predicting the near wall dynamics of particles with different shapes in a laminar flow. For the lattice Boltzmann modelling, a Gauss-Hermite projection is used to derive the lattice equation; wall boundary conditions are imposed through the Zou-He framework; and a moving least squares algorithm accurately reconstructs the forcing term accounting for the immersed boundary. First, the computational code is validated against two well-known test cases: the sedimentation of circular and elliptical cylinders in a quiescent fluid. A very good agreement is observed between the present results and those available in the literature. Then, the transport of circular, elliptical, rectangular, square and triangular particles is analyzed in a Couette flow, at Re=20. All particles drifted laterally across the stream lines reaching an equilibrium position, independently of the initial conditions. For this large Reynolds number, the particle shape has no significant effect on the final equilibrium position but it does affect the absolute value and periodicity of the angular velocity. Specifically, elongated particles show longer oscillation periods and, most interestingly, larger variations in angular velocity. The longest particles exhibit a zero angular velocity for almost the whole rotational period. Collectively, this data demonstrates that the proposed approach can be efficiently used for predicting complex particle dynamics in biologically relevant flows. This computational strategy could have significant impact in the field of computational nanomedicine for optimizing the specific delivery of therapeutic and imaging agents.
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