TY - JOUR
T1 - Fully resolved immersed electrohydrodynamics for particle motion, electrolocation, and self-propulsion
AU - Bhalla, Amneet Pal Singh
AU - Bale, Rahul
AU - Griffith, Boyce E.
AU - Patankar, Neelesh A.
N1 - Funding Information:
A.P.S.B. acknowledges helpful discussions with James Snyder and Yoni Silverman over the course of this work. B.E.G. acknowledges research support from the National Science Foundation (NSF awards DMS-1016554 and OCI-1047734 ). A.P.S.B. and N.A.P. acknowledge research support from the National Science Foundation (NSF awards CBET-0828749 , CBET-1066575 , and CMMI-0941674 ).
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Simulating the electric field-driven motion of rigid or deformable bodies in fluid media requires the solution of coupled equations of electrodynamics and hydrodynamics. In this work, we present a numerical method for treating such equations of electrohydrodynamics in an immersed body framework. In our approach, the electric field and fluid equations are solved on an Eulerian grid, and the immersed structures are modeled by meshless collections of Lagrangian nodes that move freely through the background Eulerian grid. Fluid-structure interaction is handled by an efficient distributed Lagrange multiplier approach, whereas the body force induced by the electric field is calculated using the Maxwell stress tensor. In addition, we adopt an adaptive mesh refinement (AMR) approach to discretizing the equations that permits us to resolve localized electric field gradients and fluid boundary layers with relatively low computational cost. Using this framework, we address a broad range of problems, including the dielectrophoretic motion of particles in microfluidic channels, three-dimensional nanowire assembly, and the effects of rotating electric fields to orient particles and to separate cells using their dielectric properties in a lab-on-a-chip device. We also simulate the phenomenon of electrolocation, whereby an animal uses distortions of a self-generated electric field to locate objects. Specifically, we perform simulations of a black ghost knifefish that tracks and captures prey using electrolocation. Although the proposed tracking algorithm is not intended to correspond to the physiological tracking mechanisms used by the real knifefish, extensions of this algorithm could be used to develop artificial "electrosense" for underwater vehicles. To our knowledge, these dynamic simulations of electrolocation are the first of their kind.
AB - Simulating the electric field-driven motion of rigid or deformable bodies in fluid media requires the solution of coupled equations of electrodynamics and hydrodynamics. In this work, we present a numerical method for treating such equations of electrohydrodynamics in an immersed body framework. In our approach, the electric field and fluid equations are solved on an Eulerian grid, and the immersed structures are modeled by meshless collections of Lagrangian nodes that move freely through the background Eulerian grid. Fluid-structure interaction is handled by an efficient distributed Lagrange multiplier approach, whereas the body force induced by the electric field is calculated using the Maxwell stress tensor. In addition, we adopt an adaptive mesh refinement (AMR) approach to discretizing the equations that permits us to resolve localized electric field gradients and fluid boundary layers with relatively low computational cost. Using this framework, we address a broad range of problems, including the dielectrophoretic motion of particles in microfluidic channels, three-dimensional nanowire assembly, and the effects of rotating electric fields to orient particles and to separate cells using their dielectric properties in a lab-on-a-chip device. We also simulate the phenomenon of electrolocation, whereby an animal uses distortions of a self-generated electric field to locate objects. Specifically, we perform simulations of a black ghost knifefish that tracks and captures prey using electrolocation. Although the proposed tracking algorithm is not intended to correspond to the physiological tracking mechanisms used by the real knifefish, extensions of this algorithm could be used to develop artificial "electrosense" for underwater vehicles. To our knowledge, these dynamic simulations of electrolocation are the first of their kind.
KW - Adaptive mesh refinement
KW - Dielectrophoresis
KW - Distributed Lagrange multipliers
KW - Electrolocation
KW - Fluid-structure interaction
KW - Free swimming
KW - Immersed boundary method
KW - Knifefish
KW - Nanowires
KW - Self-assembly
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U2 - 10.1016/j.jcp.2013.08.043
DO - 10.1016/j.jcp.2013.08.043
M3 - Article
AN - SCOPUS:84884319412
SN - 0021-9991
VL - 256
SP - 88
EP - 108
JO - Journal of Computational Physics
JF - Journal of Computational Physics
ER -