Linear Motility Maps in Nonlinear Viscous Fluids
Systems moving in low Reynolds number fluid regimes are known to be governed by a `motility map'' which linearly relates their shape change rates to they body frame velocity moving through the fluid. A consequence of this is Purcell's Scallop Theorem'' -- a locomotion system that undergoes shape changes that follow the same path forward and backward in time (reciprocal body deformations) cannot achieve net displacement, regardless of pacing of those changes.We show that linear-in-velocity motility maps extend to any power law viscosity (a.k.a. Ostwald--de Waele fluid), and therefore to many biological fluids in intermediate shear ranges. We also show that the linear-in-velocity property can be violated in Carreau-Yasuda fluids to produce net motion using an inchworm'' model consisting of two unequal masses with unequal drag coefficients performing reciprocal motions. Interestingly, the direction of motion can be switched by changing speeds. Our results show that the linear motility map of geometric mechaincs can be used to analyze and design locomotion in power-law fluids, and that some nonlinear drag relationships such as Carreau-Yasuda can be exploited to generate net locomotion in seeming violation of the `scallop theorem''.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Improving Pseudo-Time Stepping Convergence for CFD Simulations With Neural Networks
Computational fluid dynamics (CFD) simulations of viscous fluids described by the Navier-Stokes equations are considered. Depending on the Reynolds number of the flow, the Navier-Stokes equations may exhibit a highly non…
Surface deformation and shear flow in ligand mediated cell adhesion
We present a single, unified, multi-scale model to study the attachment\detachment dynamics of two deforming, near spherical cells, coated with binding ligands and subject to a slow, homogeneous shear flow in a viscous f…
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonline…
Reinforcement Learning (RL)Predicting waves in fluids with deep neural network
In this paper, we present a deep learning technique for data-driven predictions of wave propagation in a fluid medium. The technique relies on an attention-based convolutional recurrent autoencoder network (AB-CRAN). To …
DenoisingTime Series AnalysisCell motility modes are selected by the interplay of mechanosensitive adhesion and membrane tension
The initiation of directional cell motion requires symmetry breaking that can happen both with or without external stimuli. During cell crawling, forces generated by the cytoskeleton and their transmission through mechan…