Simulation of microswimmer hydrodynamics with multiparticle collision dynamics*

Abstract

In this review we discuss the recent progress in the simulation of soft active matter systems and in particular the hydrodynamics of microswimmers using the method of multiparticle collision dynamics, which solves the hydrodynamic flows around active objects on a coarse-grained level. We first present a brief overview of the basic simulation method and the coupling between microswimmers and fluid. We then review the current achievements in simulating flexible and rigid microswimmers using multiparticle collision dynamics, and briefly conclude and discuss possible future directions.

An Introduction to Modeling Approaches of Active Matter

“…Another popular approach to simulate the dynamics of microswimmers is based on multi-particle collision dynamics (MPCD), where, in contrast to the LBM, the solvent is represented by pointlike particles which have continuous positions and velocities [178][179][180][181][182][183][184]. To model active particles, one usually combines the MPCD method for the solvent molecules with molecular dynamics (MD) simulations of the active particles, which are coupled to the solvent and are represented either as a single particle or by a quasi-continuous distribution of particles which are connected with (timedependent) springs and represent the surface of a (deformable) microswimmer [185]. The MPCD method has been used in several works to investigate, e.g., chemotactic Janus colloids [186], active particles with phoretic interactions [187], dynamics of active particles in chemically active media [188], the motion of squirmers [157,162,164], the influence of hydrodynamic interactions on phase separation in systems of microswimmers [160], collective behavior of sperm cells [189], and active particles in filament networks [190].…”

Spatial distribution order parameter prediction of collective system using graph network

“…[19] New models and applications of deep learning have constantly proposed. [20][21][22][23][24][25][26][27][28][29][30][31] However, molecular systems involve reference invariance, degeneracy and symmetry, smoothness and other inductive biases [32] that are completely different from images or texts. So it can not straightforward to apply traditional artificial neural networks architectures to deal with them directly.…”

Characteristic features of self-avoiding active Brownian polymers under linear shear flow

“…74 It has been established that fluid flow plays a decisive role in migration and accumulation of microswimmers at surfaces. 75–83 The presence of fluid-flow and active forces lead to intriguing phenomena like upstream swimming in narrow channels 84–87 and migration away from a surface. 9,87 Moreover, activity leads to substantial changes in the suspension viscosity under shear flow, as demonstrated for bacteria.…”