Bulletin of the American Physical Society
72nd Annual Meeting of the APS Division of Fluid Dynamics
Volume 64, Number 13
Saturday–Tuesday, November 23–26, 2019; Seattle, Washington
Session S31: Biological Fluid Dynamics: Collective Behavior |
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Chair: Hamid Karani, Northwestern University Room: 613 |
Tuesday, November 26, 2019 10:31AM - 10:44AM |
S31.00001: Shrinking spinning fire ant rafts Hungtang Ko, David Hu Fire ants make rafts to stay afloat during flooding seasons. The ability to respond to different fluid environments is critical to raft sustainability. To investigate the response, we built an experiment setup to create two fluid conditions for the fire ant raft: rigid-body rotation and Taylor-Couette vortex. We found that following a rapid expansion phase, the fire ant raft shrinks at a much longer time scale under all conditions. We discovered that the additional shear from the Couette vortex help stabilize the ant raft while the centrifugal force didn't have appreciable effects. Furthermore, the result suggests that rotation can inhibit the exploration behavior of individual fire ants on the raft.. [Preview Abstract] |
Tuesday, November 26, 2019 10:44AM - 10:57AM |
S31.00002: Insights on rotor ensemble dynamics using a new scalable computational platform Wen Yan, Michael Shelley Suspensions of Stokes rotors consist of immersed particles that are driven to rotate, with that rotation creating flow fields that can create large-scale coupling and dynamics. Such rotor systems are typically driven by external means, such as a rotating magnetic field. Here we study the dynamics of closely packed rotor systems using a new method that combines a high-order accurate fluid solver, based on integral equation methods, and a temporally stable particle-particle collision solver based on geometric constraint optimization. This new computational technique is scalable on parallel computer clusters and allows us to simulate the development of large-scale dynamics. We first report the internal dynamics of a monolayer consisting of 10,000 rotors, each driven by a torque perpendicular to the monolayer. This shows both large-scale collective dynamics and complex small-scale interactions. In the second example, we turn the torque sideways and find a Kelvin-Helmholtz-like instability of the monolayer induced by the particles' rotational flows and steric interactions. [Preview Abstract] |
Tuesday, November 26, 2019 10:57AM - 11:10AM |
S31.00003: On the locomotion and collective behavior of biopolymer producing bacteria Sara Malvar, Bruno Souza Carmo, Julio Romano Meneghini One of the main causes of global warming and climate change is methane, which is released to the atmosphere during oil extraction. Because of that, the need to transform natural gas into other products arises, aiming at mitigation of gases and generaton of bioproducts. The methylotrophs bacteria can use methane and methanol as carbon sources to produce biopolymers, including polyhydroxybutyrate (PHB), a promised substitute for the environment contaminant oil-derived poly-propylene. This kind of bacteria can be very effective to help to decrease PHB price production and promote its use in substitution of several environment contaminant plastics. However, deep studies regarding the microbial sequestration of methane and gas convertion into biopolymers are still lacking on the literature. The uptake of methane and methanol by the consortium of bacteria is still inefficient and the causes related to locomotion and the collective behavior of the microorganisms remain unknown. In this study, we analyze the behavior and interaction of the various bacteria that are part of the consortium, observing how the type of flow produced by the addition of activity in the system and the fluid-structure interaction modify the production of PHB. [Preview Abstract] |
Tuesday, November 26, 2019 11:10AM - 11:23AM |
S31.00004: Stealthy Movements of Micro-Swimmer Flocks Mehdi Mirzakhanloo, Mohammad-Reza Alam Here we unveil synergistic cooperation of micro-swimmers to form a stealth swarm that minimally disturbs the surrounding fluid. We call this mode of swarming the `concealed’ mode, which can be achieved when a group of swimmers actively collaborate to cancel out one another’s disturbing flows. We then demonstrate how such a concealed swarm can remain stealth while actively gathered around a favorite spot (e.g. a nutrient source), pointing toward a target (e.g. attacking a prey flock), or tracking a desired trajectory in space. Our findings also provide a clear road map to control and lead stealth flocks of swimming micro-robots formed through their active collaboration in minimally disturbing the host medium. [Preview Abstract] |
Tuesday, November 26, 2019 11:23AM - 11:36AM |
S31.00005: Collective dynamics of biomimetic run-and-turn microswimmers Hamid Karani, Gerardo Pradillo, Petia Vlahovska Suspensions of active living and artificial micro-particles exhibit diverse out-of-equilibrium phenomena. Here, we report the emergence of complex collective behaviors in a population of motile colloids externally energized by a modulated electric field. While most of previous works on collective behaviors of colloidal systems are based on active Brownian particles, we build on our previous findings on colloidal runners-and-tumblers and demonstrate that the random-walks of individual colloids transition into plethora of collective states similar to the ones observed in bacterial systems; ranging from swarms and jets to dynamic clusters and vortices. We elucidate the role of complex physical interactions between colloidal particles and show that different emergent states are identified by competing characteristic time and length scales. More specifically, we show that the time scales during the run and tumbling phases play a major role in establishing different stable collective states. Our findings show the potential for dynamic transitioning between states at constant concentration and activity (speed) of active particles by solely tuning the kinematic time and length scales of individual random walkers. [Preview Abstract] |
Tuesday, November 26, 2019 11:36AM - 11:49AM |
S31.00006: Rheology of Bacteria Superfluids in Viscous Environments Jane Chui, Karen Fahrner, Carine Douarche, Harold Auradou, Ruben Juanes Viscous environments are ubiquitous in nature and engineering applications -- such as mucous in lungs and oil recovery strategies in the earth's subsurface -- and in all these environments, bacteria also thrive. It has been well documented that active suspensions of bacteria can behave as a superfluid, in terms of reducing the viscosity of the surrounding fluid by their collective motion, but it is not known what their effect is when they are introduced to a viscous environment. Here, we investigate experimentally how viscous environments can change the ability of pusher-type bacteria (\textit{E. coli}) in creating a superfluid regime. Using a Couette rheometer, we measure stress as a function of the applied shear rate, and define the apparent viscosity of \textit{E. coli} suspensions, varying both the density of the bacteria population within the suspension and the viscosity of the suspending fluid. We find that the bacteria suspensions remain capable of behaving as a superfluid by reducing their surrounding viscosity to zero, and that changes in solvent viscosity mainly affects the range of shear rates over which the superfluid regime is possible. From the data, we assemble the ingredients needed to build a theoretical model that describes the effective viscosity of an active fluid as a function of the bacteria density and its environment (shear rate, solvent viscosity). Beyond the value for developing a theoretical model, our results open the possibility of giving an empirical guidance for numerical simulations involving bacteria and fluid flow. [Preview Abstract] |
Tuesday, November 26, 2019 11:49AM - 12:02PM |
S31.00007: Predator-Prey Interactions using Deep Reinforcement Learning Kayhan Ulgen, Siddhartha Verma Collective behavior can allow animals to effectively perform a variety of tasks which may be difficult for a single individual to accomplish. For instance, packs of wolves, nest building ants, schools of fish, all benefit to various extents from interactions among individuals. Such interactions are extremely complex and may be difficult to formulate a priori, even in the simplest of scenarios involving two or more individuals. Past studies of such collective behavior, including foraging, hunting, and schooling, have relied on simplified rule-based statistical models. We simulate predator-prey interactions between up to three independent learning agents using a reinforcement learning algorithm, where the individuals attempt to either intercept others, or try to evade capture. The algorithm allows the individuals to learn efficient strategies autonomously to accomplish a specified goal. More specifically, the agents are modeled as point particles in two dimensional space and allowed to move within a constrained region. The agents are trained using two independent neural networks, which act as distinct ‘brains’ belonging to each individual. The trained individuals exhibit behavior which resembles that observed in nature. [Preview Abstract] |
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