Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session J08: Biofluids: Locomotion Optimization |
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Chair: Mitchell Ford, Oklahoma State University-Stillwater Room: 103B |
Sunday, November 19, 2023 4:35PM - 4:48PM |
J08.00001: Exploring the Interactions Between a Free Swimmer and Near-Wall Flows Using Deep Reinforcement Learning Alejandro Alvaro, Aishwarya S Nair, Siddhartha Verma The capability to navigate in the proximity of solid surfaces while avoiding collision and maintaining high efficiency is essential to the design of underwater vehicles. In this work, the hydrodynamics of swimming in close proximity to a solid surface is explored. Near-wall interactions of neutrally buoyant, or negatively buoyant animals are similar to ground effects experienced by fixed-winged aircrafts near a horizontal surface. Therefore, the free swimmer can experience changes to lift and drag during locomotion. Reduced drag can benefit the swimmer, however, changes in lift may lead to a collision with obstacles. Additionally, swimming close to walls can reduce the range of undulatory motion, thereby limiting performance. To study these effects in detail, the current work uses deep reinforcement learning coupled with two-dimensional numerical simulations of self-propelled swimmers. The artificial swimmers utilize mechanosensory inputs similar to the lateral line in biological fish, which detect pressure and velocity in the surrounding flow. The swimmers are trained to autonomously discover optimal actions, which allow them to navigate reliably and efficiently in the presence of obstacles. The behavior exhibited will be examined to explore how the interactions between a free swimmer and near-wall flow fields affect propulsion, efficiency, and kinematics. |
Sunday, November 19, 2023 4:48PM - 5:01PM Author not Attending |
J08.00002: Abstract Withdrawn
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Sunday, November 19, 2023 5:01PM - 5:14PM |
J08.00003: Bio-inspired versus Machine-learned Adaptations to Propulsor Damage Meredith L Hooper, Morteza Gharib Natural systems of flapping propulsion display a remarkable ability to adapt to significant propulsor damage, maintaining both lift and control authority via alterations to the propulsor trajectory. To employ this desirable trait in robotic systems, one may attempt to exactly mimic these alterations. However, it is not known whether these alterations are the most efficient adaptations to damage. Biological systems are subject to additional evolutionary pressures that may not be relevant to the optimality of robotic flapping propulsion, and in addition, biological systems generally function within more limited parameter ranges. With a larger actuation search space, optimizing for efficiency alone, a robotic propulsor may adapt in a different way than natural systems to propulsor damage. In this work, we seek optimal trajectories for damaged flapping propulsors, in order to determine whether bio-inspired strategies to adapt to significant propulsor damage are indeed the most efficient. Experimental function evaluations are performed by a flexible propulsor actuated by a spherical parallel manipulator (SPM). From these evaluations, the optimal trajectory is determined via a Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). During the optimization, a portion of the propulsor is intentionally removed. How the machine learning system adapts to this damage is then compared to existing data on natural swimmers and flyers. |
Sunday, November 19, 2023 5:14PM - 5:27PM |
J08.00004: Cell motility modes are selected by the interplay of mechanosensitive adhesion and membrane tension Yuzhu Chen, Padmini Rangamani, David Saintillan The initiation of directional cell motion requires symmetry breaking that can happen both with or without external stimuli. During cell crawling on a substrate, forces generated by the cytoskeleton and their transmission through mechanosensitive adhesions to the extracellular substrate play a crucial role. In a recently proposed 1D model (Sens, PNAS 2020), a mechanical feedback loop between mechanosensitive adhesions and membrane tension was shown to be sufficient to explain spontaneous symmetry breaking and reproduce multiple motility patterns through stick-slip dynamics, without the need to account for signaling networks or cytoskeletal reorganization. We extend this model to 2D to study the interplay between cell shape and mechanics during migration. Through a local force balance along a deformable boundary, we show that the membrane tension coupled with shape change can regulate the spatiotemporal evolution of the stochastic binding of mechanosensitive adhesions. Based on this model, we perform a linear stability analysis and determine the unstable parameter regimes where spontaneous symmetry breaking can take place. Using non-linear simulations, we show that starting from a randomly perturbed circular shape, this instability can lead to various cell motility modes including gliding, zigzag, rotating, and more irregular motions by varying parameters related to the adhesion kinetics. |
Sunday, November 19, 2023 5:27PM - 5:40PM |
J08.00005: Optimizing swimming performance of tapered elastic swimmer using a parameterized genetic algorithm Christopher Jawetz, Ersan Demirer, Alexander Alexeev A key bottleneck in the computational design of elastic swimmers is the computational intensity of the fluid-structure interaction simulations. When trying to optimize a design, it is impractical to use iterative algorithms to search the enormous design space to find an optimal solution in a reasonable time. We develop a more efficient approach to evaluate swimmer designs by using a simplified kinematic swimmer model that allows us to estimate swimmer parameters correlated with swimming performance, without the need to perform three-dimensional fully coupled fluid-structure simulations. Specifically, we optimize the design of an oscillating tapered plate that propels itself forward in viscous fluid. To this end, we vary the tapering shape and oscillation frequency of the plate. The optimization is performed using an evolutionary genetic algorithm using the kinematic swimmer model for about 10,000 generations. The criteria for the optimum swimmer kinematics include the tip displacement and standing wave ratio. These results are verified by comparing them with the full-scale CFD simulations revealing good agreement. |
Sunday, November 19, 2023 5:40PM - 5:53PM |
J08.00006: Burst-and-coast swimming needs to be optimized to outperform continuous swimming Gen Li, Dmitry Kolomenskiy, Ramiro Godoy-Diana, Benjamin Thiria Many fish species employ an intermittent swim pattern, also known as the burst-and-coast swim pattern, characterized by a period of body undulation followed by a straight glide. The burst-and-coast swim pattern is presumed as a strategy to increase energy efficiency in swimming, but its quantifiable analysis proves more challenging than continuous swimming due to the involvement of additional kinematic parameters. In this study, we employ both experimental and computational approach to systematically examine the correlation between burst-and-coast gait parameter and swimming performance, alongside the role of burst-and-coast swim in energy conservation, using the red-nose tetra fish as our model species which performs body and caudal fin (BCF) propulsion. |
Sunday, November 19, 2023 5:53PM - 6:06PM |
J08.00007: Navigation of interacting swimmers using Multi-agent Reinforcement Learning Aishwarya S Nair, Alejandro Alvaro, Siddhartha Verma It is known that aquatic organisms like fish use the velocity fields generated by the wakes of obstacles or other swimmers located upstream to reduce their energy expenditure. In this work, we explore the hydrodynamic benefits of group swimming using two-dimensional simulations of artificial self-propelled swimmers, coupled with multi-agent reinforcement learning. These swimmers utilize a sensory input system that allows them to detect the velocity field and pressure on the surface of their body, which is similar to the lateral line sensing system in biological fish. Deep reinforcement learning is used as a tool to discover optimal swimming patterns at the group level as well as the individual level, as a response to different objectives and flow fields. This can be useful in distinguishing various swimming patterns and their role in achieving higher speed or efficiency, which are desirable objectives in different scenarios. The adaptations in response to changes in the surrounding flow field will be examined by training the swimmers in stationary flow, as well as using uniform inflow. These conditions are representative of conditions encountered by fish in lakes and oceans (stationary flow), as well as during long-distance migration and in rivers (uniform flow). The physical mechanisms revealed can be helpful for developing optimal strategies for efficient collective navigation and coordination of autonomous underwater vehicles. |
Sunday, November 19, 2023 6:06PM - 6:19PM |
J08.00008: Using Morphological Intelligence in Robotic Designs of Elongated Swimmers Brian Van Stratum, Jonathan Clark, Eric Barth, Kourosh Shoele The integration of morphological intelligence into machine design holds significant promise across diverse domains, encompassing animal locomotion, marine vehicle design, robotics, and energy harvesting. To advance toward this goal, we develop and calibrate a coupled fluid-structure interaction model that focuses on long slender swimming. This model incorporates M. J. Lighthill's Large Amplitude Elongated Body Theory and employs structural dynamics with a nonlinear viscoelastic beam model. The calibration of this theoretical model is accomplished by comparing it with experimental thrust results obtained from a robotic swimmer with its head clamped in a water-filled tank. With the calibrated model in hand, we gain valuable insights into embodying morphological intelligence in free-swimming scenarios. Moreover, we propose an optimized tail design that maximizes peak thrust while considering actuation constraints. |
Sunday, November 19, 2023 6:19PM - 6:32PM |
J08.00009: Trajectory optimisation for flapping foils under unsteady inflow conditions Rodrigo Vilumbrales Garcia, Gabriel D Weymouth, Bharathram Ganapathisubramani Fish can significantly improve their swimming performance if their kinematics and paths are adequately adapted to the incoming flow. When the optimum path is unknown, the first step is to predict the performance that could be obtained for several route candidates. Next, we can select the trajectory that increases the lift or efficiency. We develop several force models with Koopman-based system identification tools to predict the CL evolution of a foil executing transitions in its motion inside an unsteady incoming wake. We test the optimum-path selection capabilities by predicting the <span style="font-size:10.8333px">CL evolution for a set of route candidates, and rank them based on CL production. We find that adding physically relevant information about the wake in the models helps to find the optimum path, achieving a correlation of 90% with numerical target data. Finally, we optimize a more general form of the transition trajectory to minimize power consumption using the previously developed force models. |
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