4.7 Article

Computational modeling of magnetic particle margination within blood flow through LAMMPS

Journal

COMPUTATIONAL MECHANICS
Volume 62, Issue 3, Pages 457-476

Publisher

SPRINGER
DOI: 10.1007/s00466-017-1508-y

Keywords

Magnetic particle; Blood flow; LAMMPS; Fluid-structure interaction

Funding

  1. Department of Mechanical Engineering at the University of Connecticut
  2. GE Fellowship for Innovation
  3. National Science Foundation [ACI-1053575]

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We develop a multiscale and multiphysics computational method to investigate the transport of magnetic particles as drug carriers in blood flow under influence of hydrodynamic interaction and external magnetic field. A hybrid coupling method is proposed to handle red blood cell (RBC)-fluid interface (CFI) and magnetic particle-fluid interface (PFI), respectively. Immersed boundary method (IBM)-based velocity coupling is used to account for CFI, which is validated by tank-treading and tumbling behaviors of a single RBC in simple shear flow. While PFI is captured by IBM-based force coupling, which is verified through movement of a single magnetic particle under non-uniform external magnetic field and breakup of a magnetic chain in rotating magnetic field. These two components are seamlessly integrated within the LAMMPS framework, which is a highly parallelized molecular dynamics solver. In addition, we also implement a parallelized lattice Boltzmann simulator within LAMMPS to handle the fluid flow simulation. Based on the proposed method, we explore the margination behaviors of magnetic particles and magnetic chains within blood flow. We find that the external magnetic field can be used to guide the motion of these magnetic materials and promote their margination to the vascular wall region. Moreover, the scaling performance and speedup test further confirm the high efficiency and robustness of proposed computational method. Therefore, it provides an efficient way to simulate the transport of nanoparticle-based drug carriers within blood flow in a large scale. The simulation results can be applied in the design of efficient drug delivery vehicles that optimally accumulate within diseased tissue, thus providing better imaging sensitivity, therapeutic efficacy and lower toxicity.

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