This paper presents a novel parametric model of marine propellers based on Non-Uniform Rational B-Splines. It involves eight parameters and five categories of spanwise parameter distributions, which are utilized for determining hydrofoil and blade shapes. 20 different hydrofoils and 5 types of well-known marine propellers are employed to detect the accuracy of the proposed parametric model. Furthermore, a propeller optimization problem was addressed with the aid of the parametric model. In the propeller optimization problem, a common AU-series propeller is treated as the baseline propeller. The proposed parametric model is used for the representation and deformation of the propeller geometric model. A hydrodynamic performance evaluation model is developed based on gene expression programming. Also, non-dominated sorting genetic algorithm II is used in the applications of the propeller optimization problem. The results demonstrate that the accuracy of the proposed parametric model satisfies the engineering requirements well, and a propeller with higher efficiency than the baseline propeller can be derived by settling the propeller optimization problem.

Ship deck arrangement design is about determining the positions and dimensions of arranged objects. This paper presents the mathematical model for the ship deck arrangement optimization problem statement and how the individual's objective and constraint functions are computed. Moreover, an improved multiobjective hybrid genetic algorithm is redesigned to solve this complex nondeterministic problem and generate a set of diverse and rational deck arrangements in the early stage of ship design. An adaptive crossover operator and a novel topological replace operator invoked in this algorithm are described. Finally, the proposed algorithm is tested on a main deck arrangement optimization of an underwater detection ship. In the validation tests, the proposed algorithm is compared to the standard NSGA-II to determine its ability to produce a set of diverse and rational deck arrangements. Subsequently, the performance tests are used to determine the ability of the algorithm to work with the highly constrained arrangement problems and the efficiency of the adaptive crossover and topological replace operators.

Although two-view Takagi-Sugeno-Kang (TSK) fuzzy classifiers have shown promising performance, most existing methods rely on static fusion and lack dynamic boundary modeling. Moreover, complex coupling architectures may obscure rule-level semantics and reduce interpretability. This study proposes a residual-directed two-view deep TSK fuzzy classifier (R-TSKFC), which establishes a hierarchical two-view collaborative learning framework to overcome the limitations of static fusion and enable adaptive modeling of shared decision boundaries. A two-view training strategy is proposed to implicitly enforce semantic consistency between views and suppress view-specific drift for the parameter space. In the feature space, a residual-directed mechanism explicitly conveys boundary-relevant information between views, allowing the proposed model to focus on under-learned regions and enhance discriminative capability. Extensive experiments on seven UCI benchmark datasets and seven real-world medical datasets demonstrate that R-TSKFC achieves competitive classification performance, with consistent improvements over representative baselines. The model also exhibits competitive performance in terms of classification accuracy, generalization ability, and computational efficiency, while preserving interpretability by relying on original input features. Moreover, the residual-directed mechanism provides an auxiliary perspective for understanding the decision process at each network layer, without compromising the transparency of the learned fuzzy rules.

The configuration of the inflow plane plays a critical role in self-propulsion simulations of ships using the body force method. In this study, we investigate the impact and applicability of different inflow plane settings on the self-propulsion simulation of a full-formed ship. A Reynolds-averaged Navier-Stokes (RANS) solver is used to simulate the self-propulsion of a full-formed ship model; by changing the inflow plane’s parameters, simulation results under different conditions are obtained, and the influence of the inflow plane parameters on the self-propulsion simulation results is analysed. Based on the test results for the ship model, the optimal inflow plane parameters are identified using the TOPSIS method. Finally, the results are applied to the simulation of another full-formed ship. The calculated results obtained with the inflow plane parameter setting method proposed in this study are found to be more in line with the test results, and the calculation accuracy is better than with the conventional recommended parameter setting method. This study provides an effective suggestion for better use of the body force method to accurately predict the self-propulsion performance of a full formed ship.