Energy-Efficient Motion Control in Fluid-Structure interaction

Energy-efficient motion through a fluid requires coordinating body movement with fluid forces and torques. This task is challenging because body motion changes the surrounding flow, which in turn affects how the body moves. Here, we study body motion control in flowing channels using a training dataset generated with computational fluid dynamics (CFD). The dataset covers two body shapes, three channel geometries, and both sensor-free and sensor-based settings. We consider direct trajectory control and force-and-torque control within a common framework that enables comparisons of physical predictors and control generators across different settings. For sensor-free circular-body control, experiments identify paths using up to 25.7% less work than the corresponding best training trajectories and demonstrate feasible planning with unseen endpoints. For sensor-based square-body control, the best neural controllers use 36.6% and 64.4% less work than the corresponding best training trajectories in the unobstructed and central-obstacle channels, respectively, and 32.3% less in the more complex two-wall channel. We hope this work will help extend the use of physical operators beyond forward prediction to more challenging control tasks.