Xianrui Hou

To achieve safe navigation and intelligent control of underwater vehicles, it is essential to establish a mathematical model that can accurately describe their maneuvering motions. In this study, the hydrodynamic performance of a designed underwater vehicle was investigated using STAR-CCM+, and a three-degree-of-freedom (3-DOF) horizontal-plane maneuvering model was developed. Computational Fluid Dynamics (CFD) simulations covering straight-line resistance, oblique towing, and typical captive-motion cases were used to identify the key hydrodynamic derivatives required by the model. The resulting coefficients were then applied to predict horizontal-plane maneuvers and were validated against CFD-based self-propulsion simulations. The comparisons show that the baseline 3-DOF model provides reasonable maneuvering predictions; however, discrepancies remain when the vehicle experiences rotational motion. By further examining the self-propulsion flow field in turning conditions, a yaw-rate-related rudder inflow effect was identified. Importantly, even when only straight-line rudder-force test data are available, introducing an effective rudder angle correction term to account for the angular-velocity-induced inflow at the rudder significantly improves prediction accuracy, bringing the maneuvering responses into closer agreement with the self-propulsion CFD results. Overall, the improved model demonstrates reliable capability for predicting the horizontal-plane maneuvering behavior of the designed underwater vehicle.

The built-in direct-drive wave energy converter (WEC) can be installed in the ocean data buoy cabin to achieve efficient self-power supply. In the traditional uncontrolled state, its high-weight core power generation unit (CPGU) is prone to collision with the protection spring, causing the thrust of the CPGU to be greater than the maximum magnetic thrust of the magnetic lead screw (MLS), resulting in a magnetic slip phenomenon, which hinders the improvement of the built-in WEC power generation capacity. This article proposes a new active motion control strategy for the built-in direct-drive WEC. This control strategy controls the movement speed of the CPGU by adjusting the load damping to eliminate the collision between the high-weight CPGU and the spring. Therefore, the power generation capacity of the built-in direct-drive WEC can be improved by increasing the operable weight of the CPGU on the original MLS. The built-in direct-drive WEC prototype was fabricated in the laboratory and experimentally verified on a six-degree-of-freedom (6-DOF) platform. Experimental data show that under the active motion control strategy, the operable weight of the CPGU increases up to 90 kg, and the average power generation of the system reaches 7.28 W.

In this study, a short-term prediction method for ship roll motion in waves based on convolutional neural network (CNN) is presented. Firstly, based on the ship roll motion equation, the data for free roll attenuation motion in still water, roll motion in regular waves, and roll motion excited by irregular waves are simulated, respectively. Secondly, the simulation data is normalized and preprocessed, and then the time-sliding window technique is applied to construct the training and testing sample sets. Thirdly, the CNN model is trained by learning from the constructed training sample sets, and the well-trained CNN model is applied to predict the roll motion. To validate the CNN model’s prediction accuracy and effectiveness, a comparison between the forecasted results and the simulation data is conducted. Meanwhile, the predicted results are also compared with that of the long-short-term memory (LSTM) neural network. The research results demonstrate that CNN can effectively achieve accurate prediction of ship roll motion in waves, and its prediction accuracy is the same as that of the LSTM neural network.

In order to simulate or control the coupled heave–pitch motion of ships in waves as realistically as possible, an appropriate mathematical model must be established in advance. In this paper, a nonparametric identification method, based on a combination of a random decrement technique (RDT) and support vector regression (SVR), was proposed to model the coupled heave–pitch motion of ships by only using the measured random responses at sea. First, a mathematical model was established to describe the coupled heave–pitch motion of ships in irregular waves. Second, the random decrement equation and the random decrement signatures were obtained by using RDT. Third, the damped frequency of the coupled heave–pitch motions were obtained by analyzing the random decrement signatures. Fourth, SVR was applied to identify the unknown hydrodynamic functions in the established mathematical model. The applicability and validity of the proposed nonparametric identification method were verified by case studies which were designed based on the simulated data and the model test data, respectively. Results of the study showed that the nonparametric identification method can be applied to identify the coupled heave–pitch motion of ships by only using the measured random responses in irregular waves.

To investigate the influence of the number of inlet pipes on the characteristics of flow field and the discharge of solid particles such as bait residue and fish feces, STAR-CCM + was used to analyze the flow of field distribution in a ship-based aquaculture cabin under the conditions of two and four inlet pipes, thus optimizing the inlet pipes of the ship-based aquaculture cabin. Furthermore, a three-phase flow model (solid–liquid–gas) was established to determine the movement characteristics of similar solid particles of residual bait and fish manure in the cabin, while the emission performance of solid particles was analyzed. Finally, the formation of solid particles in the center of the aquaculture cabin was simulated. The simulation results show that the effect of flow field is better with two inlet pipes, and the discharge volume of the bottom outlet has little impact on the emission of solid particles. Findings from this study can serve as a reference and basis for the design and optimization of the aquaculture cabin of an aquaculture vessel.

The increasing frequency of serious environmental issues and high fuel costs have necessitated better energy efficiency in the shipping industry. According to a report by the International Maritime Organization (IMO), ship speed optimization has huge potential. This paper introduces an optimization model of ship speed based on maneuvering control. The trajectory tracking theory and sliding mode control method are applied to simulate the control load of a ship. The propeller mode and rudder mode are used to calculate the corresponding rotating speed of the propeller, the rudder angle and the energy consumption. The accuracy and robustness of the maneuvering control model are validated in a still water scenario and a level 5 sea state scenario. On this basis, a particle swarm optimization (PSO) algorithm is applied to determine the best speed combination. The result shows that sailing at a constant speed is the most energy-saving strategy in still water. Moreover, due to the effects of wind, waves and currents, ships should sail faster when the sea is calm and slower when the sea is rough. These results are consistent with the actual experiences of a captain.