Maneuverability, which is closely related to operational performance and safety, is one of the important hydrodynamic properties of an underwater vehicle (UV), and its accurate prediction is essential for preliminary design. The purpose of this study is to analyze the turning ability of a UV while rising or submerging; the computational fluid dynamics (CFD) method was used to numerically predict the six-DOF self-propelled maneuvers of submarine model BB2, including steady turning maneuvers and space spiral maneuvers. In this study, the overset mesh method was used to deal with multi-body motion, the body force method was used to describe the thrust distribution of the propeller at the model scale, and the numerical prediction also included the dynamic deflection of the control planes, where the command was issued by the autopilot. Then, this study used the published model test results of the tank to verify the effectiveness of the CFD prediction of steady turning maneuvers, and the prediction of space spiral maneuvers was carried out on this basis. The numerical results show that the turning motion has a great influence on the depth and pitch attitude of the submarine, and a “stern heavier” phenomenon occurs to a submarine after steering. The underwater turning of a submarine can not only reduce the speed to brake but also limit the dangerous depth. The conclusion is of certain reference significance for submarine emergency maneuvers.

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.

Hull form optimization becomes prone to the curse of dimensionality as the number of design variables increases. The traditional sensitivity analysis method requires massive computational fluid dynamics (CFD) computations and analyzing the effects of all variables on the output; thus, it is extremely time-consuming. Considering this, the development of a rapid and effective dimensionality reduction method is particularly important. The Karhunen–Loève (K–L) transform method projects data from a high-dimensional space onto a low-dimensional space in the direction of the eigenvectors corresponding to large-variance eigenvalues. It extracts the principal components that represent the hull offset information to represent the hull geometric characteristics by analyzing the relationship between the variables in the sample offset matrix. The geometric information matrices of new hull forms can be rapidly reconstructed from the principal components. Compared with direct optimization methods, fewer variables are used to control the deformation of the hull form from the perspective of geometric deformation, avoid a large number of CFD calculations, and improve the efficiency of optimization. This study examined the relevant K–L matrix solution methods and the corresponding hull form reconstruction methods and proposed eigenvalue-based hull form reconstruction equations. The K–L transform method was combined with a previously developed multidisciplinary platform for a comprehensive optimization of ship hydrodynamic performance for hull form optimization, and its effectiveness was verified by using it to optimize DTMB 5415. The results showed that the K–L transform–based dimensionality reduction method significantly reduces the time consumption of optimization while maintaining an acceptable optimization performance.

During escort and convoy operations, icebreakers are often required to maneuver to open up channels or adjust routes due to the prevalence of ice floe conditions in Arctic routes. This study aimed to investigate the global ice load characteristics of the maneuvering captive motions, including constant turning motion, pure yaw motion, and pure sway motion, of the icebreaker Xue Long, using a combination of the discrete element method (DEM) and drag model. First, the method was verified using simulating Araon model tests from the Korea Institute of Ocean Science and Technology (KIOST). In addition, the maneuvering captive motions of the Xue Long model were simulated at varying turning radii, drift angles, and sway and yaw periods, which are typical but currently poorly studied maneuvering motions. Overall, the results of the study showed that the method is able to reproduce the coupling effect of the ship–ice–water system by considering ship–ice interaction and ice resistance, where the mean deviation and maximum deviation of ice resistance are 9.45% and 13.3%, respectively. The influences of the turning radius, drift angle, and sway and yaw period on the ice resistance and transverse force characteristics were studied and analyzed via ship–ice interactions. The present study provides a prediction tool for the assessment of ship maneuvering performance to assist the hull line development and model testing of icebreakers.

Hull form optimisation involves challenges such as large design spaces, numerous design variables, and high nonlinearity. Therefore, optimisation that only use global approximate models alone cannot yield desirable results. An information matrix-based method is proposed for dynamically embedded local approximate models (IM-DEAM) in this paper, which uses the Gaussian-function information matrix to extract one or more subspaces for additional sampling and a Latin hypercube design (LHD) for adaptive sampling. In addition, to prevent overfitting by global approximate models in some spaces because of the uneven distribution of the samples, local approximate models are embedded in the subspaces identified for additional sampling to enable accurate description of subspaces. The effectiveness and robustness of the method are validated and analysed by applying the proposed method to optimise mathematical functions and the hull form of the DTMB 5415. The results demonstrate that the proposed method is effective for improving the accuracies and can produce reliable optimisation results.

To address the problems in hull form optimization where an increase in design variables requires approximation models to be rebuilt from scratch and historical CFD samples are insufficiently utilized, this paper proposes an incremental approximation model construction method. Based on the additive decomposition property of high-dimensional model representation, this method breaks through the rigid structure limitations of traditional approximation models. When dimension expansion occurs in the design space, it fully inherits existing low-order component models and historical sample point databases, requiring only local supplementary sampling and incremental construction for new variables and their strong coupling terms, thereby achieving adaptive cross-dimensional updates of the approximation model. The method is validated through numerical test functions and a container ship hull line resistance optimization case study. Results show that, compared with traditional full-dimensional approximation models, this method reduces full-space resampling overhead and maintains high prediction accuracy while reducing CFD sample requirements, providing an efficient modeling approach for ship hydrodynamic optimization in high-dimensional dynamic spaces.