Against the backdrop of the rapid development of the global shipping industry and the deep advancement of “dual carbon” goals, energy transition, energy conservation, and emission reduction have become core issues in marine transportation. As a critical component of clean and renewable energy, the efficient development and utilization of wind energy are pivotal for achieving low-carbon shipping. Exhaust turbine sails, an innovative application of active suction control in marine aerodynamic propulsion, regulate boundary layer flow through active suction to enhance wind energy utilization efficiency, which has emerging as a research hotspot in the green transformation of modern shipping. This paper aims to synthesize research on exhaust turbine sails. First, based on fundamental fluid mechanics principles, it analyzes the impact of boundary layer separation on the aerodynamic characteristics of structural bodies. Second, through case studies, it summarizes flow control effects under different suction parameters. It further introduces combined blowing and suction control strategies to explore their influence on boundary layer management. Finally, it details the research progress of exhaust turbine sails, explaining their core principle: active suction control delays or prevents boundary layer separation, effectively suppressing vortex shedding, thereby significantly reducing ship navigation resistance and enhancing lift. The study reveals that the aerodynamic performance of exhaust turbine sails is jointly influenced by oncoming flow conditions, suction power, and structural parameters, necessitating multi-objective optimization to achieve energy efficiency balance. The paper concludes by addressing key challenges in their marine applications and envisioning future directions for integrating these sails with emerging technologies, providing practical implications for promoting the green and low-carbon transformation of the shipping industry.

To address the issue of low heading tracking efficiency caused by nonlinear dynamic characteristics in ship heading motion, this paper proposes a neural network-based adaptive hyperbolic tangent control method for ship heading. By designing a second-order system robust controller, a saturated auxiliary design system is introduced into the regulator for direct internal compensation, enhancing the system's anti-interference capability under complex operating conditions. Meanwhile, hyperbolic tangent nonlinear modification is incorporated into the control strategy to optimize the output characteristics of control signals. The controller adopts a backstepping approach to design virtual control laws for trajectory tracking and utilizes the Radial Basis Function (RBF) of neural networks to approximate the uncertain parts of the ship model. The control algorithm is simulated and tested in the MATLAB environment, and its tracking effect is analyzed. Simulation results show that the control algorithm can ensure the stability of the closed-loop system under conditions of dynamic changes in system parameters, external disturbances, and uncertainties, and effectively solve the nonlinear problems in ship traffic control during trajectory tracking. The controller is designed concisely, meets the requirements of engineering practice, improves ship maneuverability, and has reference value for ship control.

Aiming at the control problem of nonlinear uncertain systems with asymmetric saturated actuators and u nknown external disturbances, a composite control method integrating dynamic surface control (DSC), ad aptive neural network estimation, and a nonlinear saturation compensation mechanism is proposed. In the scenarios of ship course and trajectory tracking, the system faces multiple challenges such as symmetric and asymmetric actuator saturation, as well as unknown external disturbances. Radial basis function (R BF) neural networks are utilized for online approximation of unknown nonlinear functions and external d isturbances. Combined with dynamic surface technology, the problem of "explosion of complexity" in tra ditional backstepping control is eliminated. A nonlinear function with inverse correlation to error gain is designed to dynamically adjust the control gain, balancing the requirements of tracking accuracy and sat uration suppression. Furthermore, a Gaussian error function is introduced to construct a continuously diff erentiable asymmetric saturation model. An auxiliary dynamic system is integrated to compensate for the saturation nonlinear effect, achieving smooth amplitude limitation of rudder angle commands. Comparati ve MATLAB simulation results demonstrate that the course tracking error is reduced by 1°, the fluctuati on amplitude of the rudder angle is decreased by approximately 50%, the number of rudder angle satura tion events is reduced by about 60%, and the error convergence time is shortened by roughly 30%. The proposed composite control method effectively addresses the issues of asymmetric saturation and externa l disturbances, significantly enhancing the accuracy and robustness of the ship course control system.

In the context of the global shipping industry's transition towards high efficiency and low carbon emissions, energy conservation and drag reduction for ships have become core research directions in marine engineering. Container ships, as the backbone of international trade, experience a significant increase in wind resistance under extreme wind conditions of level 8 and above, which affects their navigation efficiency, energy consumption, and safety. Optimizing wind resistance is crucial for enhancing ship performance and reducing carbon emissions. The fairing can reduce the air resistance of ships by optimizing the flow field and suppressing vortex flows, presenting broad application prospects. However, existing research has primarily focused on conventional wind conditions, and further analysis is needed under extreme wind conditions. Given the typicality and harmfulness of level 8 winds, this paper takes large container ships as the research object. Based on Computational Fluid Dynamics (CFD) numerical simulations, by establishing key structural models, optimizing computational domains and grids, and selecting the Realizable k-ε turbulence model and Volume of Fluid (VOF) multiphase flow model, this study investigates the drag reduction effect of polygonal curved fairings under level 8 wind speeds. It analyzes parameters such as drag coefficient and flow field distribution, reveals the flow field regulation mechanism, and provides theoretical support and data reference for the optimal design and engineering application of fairings.

This paper investigates the problem of ship course control in the presence of model uncertainties, external disturbances, and actuator saturation. A high-performance autopilot is developed based on a direct neural network adaptive dynamic surface control (DSC) framework integrated with deep reinforcement learning. To compensate for lumped uncertainties arising from unmodeled dynamics and disturbances, a radial basis function (RBF) neural network is employed to provide online approximation within the control design. Moreover, the actuator saturation constraint is explicitly incorporated into the controller, avoiding performance degradation commonly encountered in conventional DSC schemes.To alleviate the reliance on manual parameter tuning, the controller parameter adaptation is formulated as a continuous-action optimization problem and solved using a deep deterministic policy gradient (DDPG) algorithm. The DDPG agent learns an optimal tuning policy by maximizing a reward function that penalizes course tracking errors, excessive control variations, and energy consumption. Simulation results demonstrate that the proposed method achieves improved tracking accuracy, smoother control inputs, and enhanced robustness under complex operating conditions, thereby validating the effectiveness of the DDPG-based adaptive tuning strategy for autonomous ship navigation.

This paper investigates the problem of ship course control in the presence of model uncertainties, external disturbances, and actuator saturation. A high-performance autopilot is developed based on a direct neural network adaptive dynamic surface control (DSC) framework integrated with deep reinforcement learning. To compensate for lumped uncertainties arising from unmodeled dynamics and disturbances, a radial basis function (RBF) neural network is employed to provide online approximation within the control design. Moreover, the actuator saturation constraint is explicitly incorporated into the controller, avoiding performance degradation commonly encountered in conventional DSC schemes.To alleviate the reliance on manual parameter tuning, the controller parameter adaptation is formulated as a continuous-action optimization problem and solved using a deep deterministic policy gradient (DDPG) algorithm. The DDPG agent learns an optimal tuning policy by maximizing a reward function that penalizes course tracking errors, excessive control variations, and energy consumption. Simulation results demonstrate that the proposed method achieves improved tracking accuracy, smoother control inputs, and enhanced robustness under complex operating conditions, thereby validating the effectiveness of the DDPG-based adaptive tuning strategy for autonomous ship navigation.