Large scale Micro-Photometry for high resolution pH-characterization during electro-osmotic pumping and modular micro-swimming

Micro-fluidic pumps as well as artificial micro-swimmers are conveniently realized exploiting phoretic solvent flows based on local gradients of temperature, electrolyte concentration or pH. We here present a facile micro-photometric method for monitoring pH gradients and demonstrate its performance and scope on different experimental situations including an electro-osmotic pump and modular micro-swimmers assembled from ion exchange resin beads and polystyrene colloids. In combination with the present microscope and DSLR camera our method offers a 2 \mu m spatial resolution at video frame rate over a field of view of 3920x2602 \mu m^2. Under optimal conditions we achieve a pH-resolution of 0.05 with about equal contributions from statistical and systematical uncertainties. Our quantitative micro-photometric characterization of pH gradients which develop in time and reach out several mm is anticipated to provide valuable input for reliable modeling and simulations of a large variety of complex flow situations involving pH-gradients including artificial micro-swimmers, microfluidic pumping or even electro-convection.Comment: 5 figures, 15 page

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Vortex-enhanced propulsion

It has been previously suggested that the generation of coherent vortical structures in the near-wake of a self-propelled vehicle can improve its propulsive efficiency by manipulating the local pressure field and entrainment kinematics. This paper investigates these unsteady mechanisms analytically and in experiments. A self-propelled underwater vehicle is designed with the capability to operate using either steady-jet propulsion or a pulsed-jet mode that features the roll-up of large-scale vortex rings in the near-wake. The flow field is characterized by using a combination of planar laser-induced fluorescence, laser Doppler velocimetry and digital particle-image velocimetry. These tools enable measurement of vortex dynamics and entrainment during propulsion. The concept of vortex added-mass is used to deduce the local pressure field at the jet exit as a function of the shape and motion of the forming vortex rings. The propulsive efficiency of the vehicle is computed with the aid of towing experiments to quantify hydrodynamic drag. Finally, the overall vehicle efficiency is determined by monitoring the electrical power consumed by the vehicle in steady and unsteady propulsion modes. This measurement identifies conditions under which the power required to create flow unsteadiness is offset by the improved vehicle efficiency. The experiments demonstrate that substantial increases in propulsive efficiency, over 50 % greater than the performance of the steady-jet mode, can be achieved by using vortex formation to manipulate the near-wake properties. At higher vehicle speeds, the enhanced performance is sufficient to offset the energy cost of generating flow unsteadiness. An analytical model explains this enhanced performance in terms of the vortex added-mass and entrainment. The results suggest a potential mechanism to further enhance the performance of existing engineered propulsion systems. In addition, the analytical methods described here can be extended to examine more complex propulsion systems such as those of swimming and flying animals, for whom vortex formation is inevitable

Towards an analytical description of active microswimmers in clean and in surfactant-covered drops

Geometric confinements are frequently encountered in the biological world and strongly affect the stability, topology, and transport properties of active suspensions in viscous flow. Based on a far-field analytical model, the low-Reynolds-number locomotion of a self-propelled microswimmer moving inside a clean viscous drop or a drop covered with a homogeneously distributed surfactant, is theoretically examined. The interfacial viscous stresses induced by the surfactant are described by the well-established Boussinesq-Scriven constitutive rheological model. Moreover, the active agent is represented by a force dipole and the resulting fluid-mediated hydrodynamic couplings between the swimmer and the confining drop are investigated. We find that the presence of the surfactant significantly alters the dynamics of the encapsulated swimmer by enhancing its reorientation. Exact solutions for the velocity images for the Stokeslet and dipolar flow singularities inside the drop are introduced and expressed in terms of infinite series of harmonic components. Our results offer useful insights into guiding principles for the control of confined active matter systems and support the objective of utilizing synthetic microswimmers to drive drops for targeted drug delivery applications.Comment: 19 pages, 7 figures. Regular article contributed to the Topical Issue of the European Physical Journal E entitled "Physics of Motile Active Matter" edited by Gerhard Gompper, Clemens Bechinger, Holger Stark, and Roland G. Winkle

Defect Dynamics in Active Smectics Steered by Extreme Confinement

The persistent dynamics in systems out of equilibrium, particularly those characterized by annihilation and creation of topological defects, is known to involve complicated spatiotemporal processes and thus deemed difficult, if even possible, to control. Here the steering of defect dynamics in active smectic layers exposed to extreme confinements is explored, through self-propulsion of active particles and a variety of confining geometries with different topologies. We discover a wealth of dynamical behaviors during the evolution of complex spatiotemporal defect patterns that can be steered by the confining shape and topology, particularly a perpetual creation-annihilation dynamical state with huge fluctuations of topological defects and a transition from oscillatory to damped time correlation of defect number density via the mechanism governed by boundary cusps. Our results are obtained by using an active phase field crystal approach of self-propelled stripes. Possible experimental realizations are also discussed.Comment: 6 pages, 4 figures, and 4 pages of supplemental materia

Contagion dynamics in self-organized systems of self-propelled agents

We investigate the susceptible–infectious–recovered contagion dynamics in a system of self-propelled particles with polar alignment. Using agent-based simulations, we analyze the outbreak process for different combinations of the spatial parameters (alignment strength and Peclet number) and epidemic parameters (infection-lifetime transmissibility and duration of the individual infectious period). We show that the emerging spatial features strongly affect the contagion process. The ordered homogeneous states greatly disfavor infection spreading, due to their limited mixing, only achieving large outbreaks for high values of the individual infectious duration. The disordered homogeneous states also present low contagion capabilities, requiring relatively high values of both epidemic parameters to reach significant spreading. Instead, the inhomogeneous ordered states display high outbreak levels for a broad range of parameters. The formation of bands and clusters in these states favor infection propagation through a combination of processes that develop inside and outside of these structures. Our results highlight the importance of self-organized spatiotemporal features in a variety of contagion processes that can describe epidemics or other propagation dynamics, thus suggesting new approaches for understanding, predicting, and controlling their spreading in a variety of self-organized biological systems, ranging from bacterial swarms to animal groups and human crowds

Design principles for transporting vesicles with enclosed active particles

We use coarse-grained molecular dynamics simulations to study the motility of a 2D vesicle containing self-propelled rods, as a function of the vesicle bending rigidity and the number density, length, and activity of the enclosed rods. Above a threshold value of the rod length, distinct dynamical regimes emerge, including a dramatic enhancement of vesicle motility characterized by a highly persistent random walk. These regimes are determined by clustering of the rods within the vesicle; the maximum motility state arises when there is one long-lived polar cluster. We develop a scaling theory that predicts the dynamical regimes as a function of control parameters, and shows that feedback between activity and passive membrane forces govern the rod organization. These findings yield design principles for building self-propelled superstructures using independent active agents under deformable confinement.Comment: 8 pages, 5 figure