Reliability Assessment of Hull Forms Susceptible to Parametric Roll in Irregular Seas

Traditionally ships are designed to be symmetric about their centerline which makes head seas a very safe heading for roll motion stability. However, in the recent years several incidents of large amplitude roll motion in head seas have been reported which have later been attributed to parametric roll. Parametric roll motion is a phenomenon in which a ship exhibits a large amplitude of roll motion even when it is moving into head seas with no direct excitation. This phenomenon is particularly an issue for modern high-speed fine form container ships and has gained attention relatively recently. This instability is dangerous because of its manifestation in counter-intuitive headings. Also the roll amplitude during parametric roll rises exponentially with time which gives ship captains and masters very less time to react. While this instability has been studied extensively in regular waves, its manifestation in irregular seas has not received sufficient attention. This dissertation aims at the development of design criteria based on analytical techniques which can help a designer quickly quantify the stability of a vessel to parametric excitation. For accurate simulation of parametric response of a vessel/platform in irregular seas, an in-house time domain simulation program has been developed and validated against available experiments. The roll equation of motion is then simplified into a single degree of freedom model for analytical assessment. The existing single degree of freedom models in the literature are compared against the time domain simulation tool to gain an understanding of the extent to which the simplified models capture the dynamics of the phenomenon. In order to improve the roll modeling, a new approach is suggested to overcome some of the limitations of the existing models. This new model is then investigated using two analytical approaches, one from the theory of nonlinear dynamical systems and the other from stochastic dynamics to come up with two independent measures of stability. Both of these measures are used to demonstrate their potential as a design criteria which can be used by a ship designer. A comparison of the two methods for a variety of cases is undertaken to demonstrate the similar trends they exhibit

Navigating the Ocean with DRL: Path following for marine vessels

Human error is a substantial factor in marine accidents, accounting for 85% of all reported incidents. By reducing the need for human intervention in vessel navigation, AI-based methods can potentially reduce the risk of accidents. AI techniques, such as Deep Reinforcement Learning (DRL), have the potential to improve vessel navigation in challenging conditions, such as in restricted waterways and in the presence of obstacles. This is because DRL algorithms can optimize multiple objectives, such as path following and collision avoidance, while being more efficient to implement compared to traditional methods. In this study, a DRL agent is trained using the Deep Deterministic Policy Gradient (DDPG) algorithm for path following and waypoint tracking. Furthermore, the trained agent is evaluated against a traditional PD controller with an Integral Line of Sight (ILOS) guidance system for the same. This study uses the Kriso Container Ship (KCS) as a test case for evaluating the performance of different controllers. The ship's dynamics are modeled using the maneuvering Modelling Group (MMG) model. This mathematical simulation is used to train a DRL-based controller and to tune the gains of a traditional PD controller. The simulation environment is also used to assess the controller's effectiveness in the presence of wind.Comment: Proceedings of the Sixth International Conference in Ocean Engineering (ICOE2023

Comparison of path following in ships using modern and traditional controllers

Vessel navigation is difficult in restricted waterways and in the presence of static and dynamic obstacles. This difficulty can be attributed to the high-level decisions taken by humans during these maneuvers, which is evident from the fact that 85% of the reported marine accidents are traced back to human errors. Artificial intelligence-based methods offer us a way to eliminate human intervention in vessel navigation. Newer methods like Deep Reinforcement Learning (DRL) can optimize multiple objectives like path following and collision avoidance at the same time while being computationally cheaper to implement in comparison to traditional approaches. Before addressing the challenge of collision avoidance along with path following, the performance of DRL-based controllers on the path following task alone must be established. Therefore, this study trains a DRL agent using Proximal Policy Optimization (PPO) algorithm and tests it against a traditional PD controller guided by an Integral Line of Sight (ILOS) guidance system. The Krisco Container Ship (KCS) is chosen to test the different controllers. The ship dynamics are mathematically simulated using the Maneuvering Modelling Group (MMG) model developed by the Japanese. The simulation environment is used to train the deep reinforcement learning-based controller and is also used to tune the gains of the traditional PD controller. The effectiveness of the controllers in the presence of wind is also investigated.Comment: Proceedings of the Sixth International Conference in Ocean Engineering (ICOE2023