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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. [ABSTRACT FROM AUTHOR]

Introducing of thermodynamically stable strengthening phases is an important means enhancing the properties of magnesium alloys. To introduce a stable Al2Nd strengthening phase, the Mg-Al-Nd alloys were prepared with a fixed atomic ratio of 2:1 for Al and Nd. The total content of Al and Nd was varied at 1%, 3%, 5%, and 7%, respectively. The effects of Al2Nd phase on the microstructure and mechanical properties of the Mg-Al-Nd alloys were systematically studied in this work. The experimental results indicated that the as-cast alloy primarily consisted of the acicular Al11Nd3 phase as a secondary phase. After undergoing a homogenization treatment at 500 °C for 12 h, the secondary phase underwent a transformation, resulting in the formation of short rod shape Al2Nd structures. Simultaneously, layered Al2Nd precipitated within the α-Mg matrix. The formation of twinning was inhibited, while the Al2Nd phase promoted recrystallization and refined the microstructure through the mechanism of particles stimulated nucleation (PSN), as the Al and Nd contents increased during thermal deformation. At the same time, the increases of Al2Nd phase can enhance the strength and the plasticity of the alloy. The mechanical properties of extruded Mg-1.36Al-3.64Nd (AE5) alloy were excellent with yield strength of 175 MPa, ultimate tensile strength of 249 MPa, and the elongation of 15.4%. The enhanced performance can be primarily attributed to the refined microstructure and the presence of Al2Nd precipitates with small size, which contributed to effective fine grain strengthening and secondary phase strengthening. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]