Active wave energy extraction by hydrofoil vessels through deep reinforcement learning-based flight control

Ocean Engineering · 2024 · 3 citations

• Artificial Intelligence
• Computer Science
• Marine engineering

The hydrofoil vessel is among the few types of ships known to demonstrate negative added resistance in waves. This paper investigates the potential for maximizing this effect through the development of a neural network-based flight control system trained via reinforcement learning to actively extract wave energy. Furthermore, it examines the impact of such a control system on passenger comfort, and whether a feasible trade-off can be made between efficiency and comfort. The results suggest that significant energy savings can be achieved through the proposed methodology, achieving a power reduction of 61.8 % compared to that in calm water, for a simplified model of a hydrofoil vessel operating in 1.5 m regular head waves of 5 s wave period. If optimizing for passenger comfort, a reduction in vertical accelerations of 98% can be achieved, however, at the cost of achieving virtually no power reduction as compared to calm-water operation. By using a reward function with weighted focus on comfort and energy efficiency, it was demonstrated that the presented methodologies can produce a good trade-off between these two performance indicators. The presented cases differ only in terms of the flight control software, demonstrating the profound potential for altering hydrofoil vessel performance through intelligent control. • A method for DRL-based hydrofoil flight control. • Significant negative added resistance in waves, < −60%. • Cancellation of wave-induced motions with no resistance penalty. • Effective balancing of efficiency and comfort. • Discussions of the physics of negative added resistance of hydrofoil vessels.

Drag Forces on Plastic Bag Marine Debris

2024

• Marine engineering
• Environmental science
• Geology

Plastic debris, including thin-film plastic bags, pose physical, chemical, and biological threats to marine life. Effective marine plastic removal strategies require an understanding of long-range debris trajectories, which are driven by hydrodynamic drag characteristics. For this study, drag forces were measured by towing a standard, plastic grocery bag through a water tank at varied speeds. Drag force profiles of the plastic bag sample for constant and sinusoidal speed tests in the tow tank demonstrated a parabolic relationship between speed and drag force that accurately predicted horizontal drag in sinusoidal motion. Results can inform MIT Sea Grant's design of a spatially tracked marine surface drifter mimicking hydrodynamic behavior of plastic bags, providing an empirical basis for predicting plastic bag accumulation zones and facilitating strategic removal.