De Gruyter | 270 years of academic publishing

This study investigates the WAM-V (Wave Adaptive Modular Vessel) catamaran configuration, focusing on the hydrodynamic interaction between its articulated hulls. The unique hinged connection mechanism induces a relative angular displacement between the demihulls during operation, significantly modifying the calm water resistance characteristics. Such resistance variations critically influence both vessel maneuverability and the operational effectiveness of onboard acoustic detection systems. This study using computational fluid dynamics (CFD) technology, the effects of varying demihull spacing and the angles of the demihulls on resistance were calculated. Numerical simulations were performed using STAR-CCM+, employing the Reynolds-averaged Navier–Stokes equations (RANS) method combined with the k-epsilon turbulence model. The study investigates the free surface and double body viscous flow at different Froude numbers in the range of 0.3 to 0.75. The analysis focuses on the effects of the demihull spacing ratio (B_S/L_PP, Demihull spacing/Length between perpendiculars) on calm water resistance. Specifically, the resistance coefficient at B_S/L_PP = 0.2 is on average 14% higher than that at B_S/L_PP = 0.5. Additionally, the influence of demihull angles on resistance was simulated at B_S/L_PP = 0.42. The results indicate that inner demihull angles result in higher resistance compared to outer angles, with the maximum increase in resistance being approximately 9%, with specific outer angles effectively reducing resistance. This study provides a scientific basis for optimizing catamaran design and offers valuable insights for enhancing sailing performance.

This paper introduces a method that can enhance the accuracy and efficiency of point cloud data registration. This method selects the centroid of the point cloud as the feature point and uses the projected distance of this feature point within the dynamic neighborhood to other points as the feature information. Through this feature information, it accomplishes the registration of two sets of point cloud data. This method increases the density and integrity of point cloud data, improves the accuracy and robustness of point cloud registration, and the selection of feature points reduces the computational load thereby enhancing processing efficiency. The introduction of the dynamic neighborhood enables the method to flexibly handle point cloud data of different scales and densities. Experimental results show that the proposed method has good performance in terms of accuracy and efficiency for achieving point cloud data registration and dealing with data under various complex conditions and can effectively improve the effect of point cloud data registration and fusion.

Trajectory tracking control of autonomous underwater vehicles (AUVs) faces challenges in complex nearshore environments due to model uncertainties and external environmental disturbances. Traditional control methods often rely on expert knowledge and manual parameter tuning, which limit the adaptability of AUVs to structural variations and changing operating conditions. Moreover, inappropriate parameter selection in conventional sliding mode control may induce high-frequency chattering, degrading control accuracy and operational efficiency. To address these issues, this paper proposes an improved integral sliding mode control (IISMC) strategy integrated with deep reinforcement learning (DRL). In the proposed framework, DRL is employed to adaptively tune key controller parameters, including the sliding surface coefficients and reaching law gains, while preserving the analytical structure of the IISMC scheme. This adaptive tuning mechanism effectively suppresses chattering and enhances robustness against uncertainties and disturbances. Numerical simulation results demonstrate that the proposed DRL-assisted IISMC method achieves improved disturbance rejection capability, higher trajectory tracking accuracy, and smoother control performance compared with conventional sliding mode control (SMC) approaches under identical operating conditions.

To address the low trajectory tracking accuracy and limited robustness of conventional reinforcement learning algorithms under complex marine environments involving wind, wave, and current disturbances, this study proposes a proximal policy optimization (PPO) algorithm incorporating an intrinsic curiosity mechanism to solve the unmanned surface vehicle (USV) trajectory tracking control problem. The proposed approach is developed on the basis of a three-degree-of-freedom (3-DOF) USV model and formulated within a Markov decision process (MDP) framework, where a multidimensional state space and a continuous action space are defined, and a multi-objective composite reward function is designed. By incorporating a pure pursuit guidance algorithm, the complexity of engineering implementation is reduced. Furthermore, an improved PPO algorithm integrated with an intrinsic curiosity mechanism is adopted as the trajectory tracking controller, in which the exploration incentives provided by the intrinsic curiosity module (ICM) guide the agent to explore the state space efficiently and converge rapidly to an optimal control policy. The final experimental results indicate that, compared with the conventional PPO algorithm, the improved PPO–ICM controller achieves a reduction of 54.2% in average lateral error and 47.1% in average heading error under simple trajectory conditions. Under the complex trajectory condition, the average lateral error and average heading error are reduced by 91.8% and 41.9%, respectively. These results effectively demonstrate that the proposed PPO–ICM algorithm attains high tracking accuracy and strong generalization capability across different trajectory scenarios, and can provide a valuable reference for the application of intelligent control algorithms in the USV domain.

This study investigates the effects of key geometric parameters on the hydrodynamic and cavitation characteristics of two-element hydrofoil systems for fully submerged unmanned hydrofoil craft, aiming to solve their active stabilization problems. Using STARCCM+ software, the RANS method, and the SST k-ω turbulence model, the research analyzes the impacts of flap deflection angle (α), main wing-to-flap chord ratio (c1/c2), and spacing (g). Results show that when the spacing is fixed, increasing the chord ratio reduces the lift and drag coefficients. When the chord ratio is fixed, increasing the spacing causes the lift and drag coefficients to first rise and then fall. With increasing flap deflection angle (α), cavitation intensifies, but it can be suppressed by increasing the chord ratio, reaching a minimum at g = 2.4%c1. The optimal configuration is c1/c2 = 1.5 and g = 2.4%c1, which can balance the lift–drag performance and anti-cavitation capability. This study provides a scientific basis for solving the active stabilization problems of fully submerged unmanned hydrofoil craft and insights for enhancing their seakeeping performance.