The particle swarm optimisation (PSO) algorithm has been widely used in hull form optimisation owing to its feasibility and fast convergence. However, similar to other intelligent algorithms, PSO also has the disadvantages of local premature convergence and low convergence performance. Moreover, optimization data are not used to analyse and reduce the range of values for relevant design variables. Our study aimed to solve these existing problems in the PSO algorithm and improve PSO from four aspects, namely data processing of particle swarm population initialisation, data processing of iterative optimisation, particle velocity adjustment, and particle cross-boundary configuration, in combination with space reduction technology. The improved PSO algorithm was used to optimise the hull form of an engineering vessel at Fn = 0.24 to reduce the wave-making resistance coefficient under static constraints. The results showed that the improved PSO algorithm could effectively improve the optimisation efficiency and reliability of PSO and effectively overcome the drawbacks of the PSO algorithm.

Recently, the NURBS technique has been widely used in the 3D design software for ships. However, in most research, the NURBS technique is only applied to the mathematical representation of hull curves and surfaces, and the parametric deformation of hull surfaces based on geometric feature parameters is less understood. The aims of this paper are to establish the parametric design process of hull surfaces through the classification of geometric feature parameters and the design of feature curves, apply the NURBS technique to the parametric geometric modeling of hull curves and surfaces, and finally achieve the parametric deformation of hull surfaces driven by geometric feature parameters and develop the parametric deformation software. Taking the Series 60 ship as an example, we first analyze the hull geometric features and parameters, then design the longitudinal feature curves and cross-section curves based on the NURBS technique and establish the correlation between them, and finally generate the smooth hull surface by the skinning technique to achieve the parametric geometric deformation of the Series 60 ship. The research in this paper shows that the smoothness of the surfaces generated by the NURBS-based parametric design method is good. Additionally, the extracted feature parameters have a clear geometric meaning and can automatically generate hull forms to meet the design requirements quickly and effectively, which has some practical engineering value.

While navigating at sea, ships are influenced by various factors, including wind, waves, and currents, which can result in heave motion that significantly impacts operations and potentially leads to accidents. Accurate forecasting of ship heaving is essential to guarantee the safety of maritime navigation. Consequently, this paper proposes a hybrid neural network method that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory Networks (BiLSTMs), and an Attention Mechanism to predict the heaving motion of ships in moderate to complex sea conditions. The data feature extraction ability of CNNs, the temporal analysis capabilities of BiLSTMs, and the dynamic adjustment function of Attention on feature weights were comprehensively utilized to predict a ship’s heave motion. Simulations of a standard container ship’s motion time series under complex sea state conditions were carried out. The model training and validation results indicate that, under sea conditions 4, 5, and 6, the CNN-BiLSTM-Attention method demonstrated significant improvements in MAPE, APE, and RMSE when compared to the traditional LSTM, Attention, and LSTM-Attention methods. The CNN-BiLSTM-Attention method could enhance the accuracy of the prediction. Heave displacement, pitch displacement, pitch velocity, pitch acceleration, and incoming wave height were chosen as key input features. Sensitivity analysis was conducted to optimize the prediction performance of the CNN-BiLSTM-Attention hybrid neural network method, resulting in a significant improvement in MAPE and enhancing the accuracy of ship motion prediction. The research presented in this paper establishes a foundation for future studies on ship motion prediction.

Basis functions are key in constructing interpolation equations in hull surface modification based on radial basis functions (RBF) interpolation. However, few have studied the selection of basis functions in depth. By comparing several typical basis functions through a theoretical analysis and two-dimensional modification examples, the Wendland ψ3,1 (W) function is selected. The advantages of hull form surface modification based on W function interpolation are further validated through a case study. Finally, the modification method is used to optimize a trimaran model. An optimal hull form with fair lines is obtained, and its wave-making resistance coefficient and total resistance are reduced by 8.3% and 3.8%, respectively, compared to those of the original model. These findings not only further illustrate that the W function is relatively suitable for hull form surface modification, but also validate the feasibility and value of the RBF interpolation-based surface modification method in engineering practice.