Hydrodynamics of suspensions of passive and active rigid particles: A rigid multiblob approach

Florencio Balboa Usabiaga, Bakytzhan Kallemov, Blaise Delmotte, Amneet Pal Singh Bhalla, Boyce E. Griffith, Aleksandar Donev

Research output: Contribution to journalArticle

Abstract

We develop a rigid multiblob method for numerically solving the mobility problem for suspensions of passive and active rigid particles of complex shape in Stokes flow in unconfined, partially confined, and fully confined geometries. As in a number of existing methods, we discretize rigid bodies using a collection of minimally resolved spherical blobs constrained to move as a rigid body, to arrive at a potentially large linear system of equations for the unknown Lagrange multipliers and rigid-body motions. Here we develop a block-diagonal preconditioner for this linear system and show that a standard Krylov solver converges in a modest number of iterations that is essentially independent of the number of particles. Key to the efficiency of the method is a technique for fast computation of the product of the blob-blob mobility matrix and a vector. For unbounded suspensions, we rely on existing analytical expressions for the Rotne-Prager-Yamakawa tensor combined with a fast multipole method (FMM) to obtain linear scaling in the number of particles. For suspensions sedimented against a single no-slip boundary, we use a direct summation on a graphical processing unit (GPU), which gives quadratic asymptotic scaling with the number of particles. For fully confined domains, such as periodic suspensions or suspensions confined in slit and square channels, we extend a recently developed rigid-body immersed boundary method by B. Kallemov, A. P. S. Bhalla, B. E. Griffith, and A. Donev (Commun. Appl. Math. Comput. Sci. 11 (2016), no. 1, 79-141) to suspensions of freely moving passive or active rigid particles at zero Reynolds number. We demonstrate that the iterative solver for the coupled fluid and rigid-body equations converges in a bounded number of iterations regardless of the system size. In our approach, each iteration only requires a few cycles of a geometric multigrid solver for the Poisson equation, and an application of the block-diagonal preconditioner, leading to linear scaling with the number of particles. We optimize a number of parameters in the iterative solvers and apply our method to a variety of benchmark problems to carefully assess the accuracy of the rigid multiblob approach as a function of the resolution. We also model the dynamics of colloidal particles studied in recent experiments, such as passive boomerangs in a slit channel, as well as a pair of non-Brownian active nanorods sedimented against a wall.

Original languageEnglish (US)
Pages (from-to)217-296
Number of pages80
JournalCommunications in Applied Mathematics and Computational Science
Volume11
Issue number2
DOIs
StatePublished - 2016

Fingerprint

Linear systems
Hydrodynamics
Lagrange multipliers
Poisson equation
Nanorods
Rigid Body
Tensors
Reynolds number
Fluids
Geometry
Scaling
Processing
Iteration
Preconditioner
Experiments
Converge
Immersed Boundary Method
Fast multipole Method
Iterative Solver
Iterative Solvers

Keywords

  • Colloidal suspensions
  • Immersed boundary method
  • Stokes flow
  • Stokesian dynamics

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Hydrodynamics of suspensions of passive and active rigid particles : A rigid multiblob approach. / Usabiaga, Florencio Balboa; Kallemov, Bakytzhan; Delmotte, Blaise; Bhalla, Amneet Pal Singh; Griffith, Boyce E.; Donev, Aleksandar.

In: Communications in Applied Mathematics and Computational Science, Vol. 11, No. 2, 2016, p. 217-296.

Research output: Contribution to journalArticle

Usabiaga, Florencio Balboa ; Kallemov, Bakytzhan ; Delmotte, Blaise ; Bhalla, Amneet Pal Singh ; Griffith, Boyce E. ; Donev, Aleksandar. / Hydrodynamics of suspensions of passive and active rigid particles : A rigid multiblob approach. In: Communications in Applied Mathematics and Computational Science. 2016 ; Vol. 11, No. 2. pp. 217-296.
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