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Considerable parameter perturbations occur owing to the influence of uncertain factors in actual ship transportation, resulting in a substantial decline in ship performance. These parameters should not be regarded as certain values but uncertain variables. Ship robust design optimization (RDO) is a method in which various uncertainties are fully considered in the early stages of ship design to ensure that the optimal case adapts to the perturbation of the uncertain parameters. In this study, instead of the commonly used Monte Carlo method, polynomial chaos expansions (PCEs) are adopted to quantify the uncertainty, and an improved probabilistic collocation method (PCM) based on the linear independence principle is proposed to select sample points for calculating polynomial coefficients of PCE, which not only reduces the number of collocation points compared with the traditional statistical sampling method but also avoids the problem that arises with the traditional PCM, which cannot maintain high calculation accuracy even with considerable collocation points. Finally, to ensure ship robustness, in comparison with deterministic optimization design, the proposed RDO framework is applied to minimum Energy Efficiency Design Index (EEDI) KRISO Container Ship hull form design.

Nowadays, system-based simulation is one of the main methods for ship manoeuvring prediction. Great efforts are usually devoted to the determination of hydrodynamic derivatives as required for the mathematical models used for such methods. System identification methods can be applied to determine hydrodynamic derivatives. The purpose of this work is to present a parameter identification study based on least-squares support-vector machines (LS-SVMs) to obtain hydrodynamic derivatives for an Abkowitz-type model. An approach for constructing training data is used to reduce parameter drift. In addition, wavelet threshold denoising is applied to filter out the noise from the sample data during data pre-processing. Most of the resulting derivatives are very close to the original ones—especially for linear derivatives. Although the errors of high-order derivatives seem large, the final predicted results of the turning circle and zigzag manoeuvres agree pretty well with the reference ones. This indicates that the used methods are effective in obtaining manoeuvring hydrodynamic derivatives.

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.

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.

Combining computational fluid dynamics and optimization techniques provides a significant method for automatic multi-scheme selection in hull form optimization, which can improve the design level of energy-efficient ships. This paper focuses on hull surface automatic modification and its application in hull form optimization. A new hull surface automatic modification method is proposed, based on the radial basis function interpolation method. Furthermore, Delaunay triangulation is applied in order to determine the support radius of the Wendland basic function. The developed hull form optimization tool is applied to the hydrodynamic design optimization of a Series 60 model, and model tests are carried out on the original and an optimised model at WHUT (Wuhan University of Technology) for validation. The numerical optimization and model tests results demonstrate that the proposed method can significantly improve optimization efficiency.

Combining an optimisation algorithm with computational fluid dynamics (CFD) technology to achieve automatic multi-scheme selection of hull lines is of considerable significance for the research and development of energy-saving ships. Parametric modification technology for the hull form is a key issue for achieving this target. The traditional parametric modification method has the disadvantage of many parameters and high computational load. This paper proposes a new ship surface modification method that combines two different techniques based on the modification and morphing functions. Using the proposed method, a hull optimisation tool is developed with CFD and a multi-objective genetic algorithm. To verify the validity of the method, optimisation of the bow of a 1300TEU container ship is performed, and the optimised hull is obtained at multi-velocity. The numerical results indicate that the proposed parametric modification method is effective in realising local and global modification of the hull form. The hull optimisation platform developed is practical for engineering applications.

The typical maneuvering of a ship can reflect its maneuvering characteristics, which are closely related to the safety and economy of its navigation. The accurate prediction of a ship’s maneuvering characteristics is essential for its preliminary design. This paper adopts the overset grid method to deal with multibody motion and the body-force method to describe the thrust distribution of the propeller at the model scale, as well as to obtain the changes in the hydrodynamic load and the characteristic parameters in a computational fluid dynamics (CFD) maneuver simulation. Then, the paper compares the results with those of a self-propulsion experiment conducted at the China Ship Scientific Research Center. The numerical results show that the maneuverability characteristics obtained from the CFD simulation are in satisfactory agreement with the experimental values, which demonstrates the applicability and reliability of the combination of the overset grid with the body-force method in the numerical prediction of the typical maneuvering of a ship. This provides an effective pre-evaluation method for the prediction of a ship’s maneuvering through self-propulsion.

Hull form optimization is a typical complex engineering problem. The complex design performance space often results in a low optimization efficiency as an optimal solution cannot be ensured. Presently, several methods, such as efficient optimization algorithms, approximate model technology, and high-performance computing are primarily used to reduce the calculation time. However, these methods cannot satisfy practical application requirements in terms of efficiency and solution accuracy. Thus, we investigated a dynamic space reduction optimization framework (DSROF), in this study, wherein data mining is continuously performed during the optimization process to dynamically reduce the range and number of variables. DSROF enables subsequent optimization only in the range that exhibits a high performance, thereby reducing redundant calculations, improving optimization efficiency, and ensuring a higher degree of accuracy. Furthermore, we applied DSROF to function examples and hull form optimization. The results indicate that the use of the DSROF can reduce the calculation cost in hull form optimization by 23% in comparison with that of the particle swarm optimization algorithm.

More and more successful applications of RANS tool to obtain hydrodynamic derivatives or coefficients for ship manoeuvring prediction have been reported in recent years. However, to obtain all required hydrodynamic derivatives from pure RANS simulations will be very time-consuming due to many cases, especially considering dynamic ship motions in water surface and real rotating propeller with dynamic grid approach. This might be one of the limiting factors to apply RANS tool for ship manoeuvring prediction widely, although the relevant numerical methods have been demonstrated to be of acceptable accuracy in many respects. So that, it is quite necessary to explore the appropriate strategies when applying RANS tool for ship manoeuvring prediction, consequently balancing prediction accuracy and time cost as far as possible. In this study, we use the own expanded RANS solver to simulate a series of captive model tests for the oil tanker KVLCC2. Assumptions of double-body flow and body force propeller are introduced during the simulations, in order to reduce computational time. Hydrodynamic derivatives are determined by regression analysis to the computed hydrodynamic forces, and free-running turning test and zig-zag test are simulated by using these derivatives. The RANS method and the whole procedure of manoeuvring prediction are described in this article. The main purpose is to check the applicability of the expanded RANS solver, and to confirm the effectiveness of whole procedure of manoeuvring prediction. It shows that the derivatives from RANS, can be used for manoeuvring prediction, at least for KVLCC2, and the present strategy of ship manoeuvring prediction is quite promising in the respect of the balance of prediction efficiency and accuracy.

In order to truly reflect the ship performance under the influence of uncertainties, uncertainty-based design optimization (UDO) for ships that fully considers various uncertainties in the early stage of design has gradually received more and more attention. Meanwhile, it also brings high dimensionality problems, which may result in inefficient and impractical optimization. Sensitivity analysis (SA) is a feasible way to alleviate this problem, which can qualitatively or quantitatively evaluate the influence of the model input uncertainty on the model output, so that uninfluential uncertain variables can be determined for the descending dimension to achieve dimension reduction. In this paper, polynomial chaos expansions (PCE) with less computational cost are chosen to directly obtain Sobol' global sensitivity indices by its polynomial coefficients; that is, once the polynomial of the output variable is established, the analysis of the sensitivity index is only the postprocessing of polynomial coefficients. Besides, in order to further reduce the computational cost, for solving the polynomial coefficients of PCE, according to the properties of orthogonal polynomials, an improved probabilistic collocation method (IPCM) based on the linear independence principle is proposed to reduce sample points. Finally, the proposed method is applied to UDO of a bulk carrier preliminary design to ensure the robustness and reliability of the ship.

In the ship design optimization process, the neglect of the unavoidable uncertainty of parameters in the actual navigation and experimental observations, may lead to a bad result with some hidden dangers in practical applications. Considering the influence of the uncertainty, a new and effective hull form reliability-based robust design optimization (RBRDO) framework, including the hull form modification module and RBRDO module, has been developed and tested in the present work. Radial basis function method is utilized as the parametric hull surface modification technique to generate a series of smooth hull forms while combining polynomial chaos expansions (PCE) method with maximum entropy method (MEM) to conduct the uncertainty analysis for the prediction of the mean and the standard deviation of the objective and the failure probability of constraints. To verify the validity of the method, hull form design optimization of the bow of KCS model is implemented under the influence of the uncertainty. Numerical results indicate that the proposed RBRDO framework is effective compared with traditional Monte Carlo method. Meanwhile, compared with traditional DO case, RBRDO case has higher adaptability to the environmental uncertainty with the lower failure probability, which ensure the robustness and reliability of the optimal hull form.

When the number of experimental points and variables in uniform design for mixture experiments is too large, the requirements of uniformity and calculation efficiency are hard to be satisfied simultaneously. In this paper based on the transformation of U-type matrix method, the uniformity is improved by cutting method, and the calculation efficiency problem is solved by genetic algorithm. So the uniform design for mixture experiments with good uniformity and arbitrary number of experimental points and variables is able to be generated. Then it is applied to the multi-objective optimization algorithm based on physical programming to improve optimization quality and generate evenly distributed Pareto front. Finally, the effectiveness of the improved uniform design for mixture experiments in multi-objective optimization is verified by a numerical example with three objectives.

This paper presents a new space model for collision avoidance, ship dynamic collision avoidance space model (SDCASM). The model reserves all information for collision avoidance and the speed of collision avoidance calculation would be quicker. To verify the validity of the model, an enhanced optimization algorithm, genetic annealing algorithm (GAA), is applied to trajectory optimization for collision avoidance based on the SDCASM. At last, model and algorithm that put forward in this paper is applied to inland waterway ship collision avoidance. And several simulations are implemented in curve sea-route of inland waterway. As a result of this study, it could offer a safe and economic trajectory for ship avoiding collision towards establishing the inland waterway ship automatic collision avoidance system.