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The shape of the ship’s forebody has a greater impact on the ship’s resistance, and a reasonable optimization of the forebody can play a role in reducing the propulsion power and optimizing the resistance performance. Under the premise of Rankine source method of potential flow wave theory as the theoretical basis, SHIPFLOW software is used as the calculation tool, and CAESES software is used as the optimization tool to study the optimal design of the ship with minimum wave resistance. In the optimization process, a real ship is taken as the object, and the optimal solution of the rising wave resistance coefficient is calculated with the rising wave resistance coefficient as the objective function and the ship speed and displacement as the constraints. The real ship is selected as the mother ship, the parameters of the hull shape are taken as the design variables, and the shape of the forebody is optimized by the Lackenby shift method, so as to obtain a ship shape with less wave resistance at the same speed and within the displacement limit. The results show that the improved ship shape has obvious effect of reducing the wave resistance, which verifies the effectiveness and feasibility of this method for ship shape optimization

The geometric design and shape optimization of complex surfaces are pivotal and knotty techniques in computer aided geometric design (CAGD), and widely used in many complex product manufacturing fields involving surfaces modeling, e.g., for ships, aircraft wing, automobiles, etc. In this paper, an enhanced golden jackal optimization (GJO) algorithm is used to optimize the shape of complex composite shape-adjustable generalized cubic Ball (CSGC-Ball, for short) surfaces. Firstly, the shape design of CSGC-Ball surfaces is mathematically an optimization problem that can be efficiently dealt with by meta-heuristic algorithms. In this regard, an enhanced GJO (EGJO), combined with opposition-based learning, spring vibration-based adaptive mutation and binomial-based cross-evolution strategy, is developed to improve the convergence speed and calculation accuracy of the original GJO. The performance of EGJO is assessed on 23 benchmark test functions, IEEE CEC-2019 and 4 actual engineering optimization problems, and the competition and practicability of EGJO algorithm are confirmed. Secondly, the CSGC-Ball surfaces with global and local shape parameters is constructed based on a class of cubic generalized Ball basis functions, and then the conditions of G1 and G2 continuity for the surfaces are derived. The shapes of CSGC-Ball surfaces can be adjusted and optimized expediently by utilizing their shape parameters. Finally, the minimum energy-based shape optimization models of CSGC-Ball surfaces with 1th-order and 2th-order geometric continuity are established, respectively. Furthermore, the proposed EGJO is utilized to solve the established optimization models, and the CSGC-Ball surfaces with minimum energy are obtained. Four representative examples are given to demonstrate the excellence and effectiveness of EGJO in solving the shape optimization problems of complex CSGC-Ball surfaces.

This paper explores the integration of advanced machine learning (ML) techniques within simulation-based design optimization (SBDO) processes for naval applications, focusing on the hydrodynamic shape optimization of the DTMB 5415 destroyer model. The use of unsupervised learning for design-space dimensionality reduction, combined with supervised learning through active learning-based multi-fidelity surrogate modeling, allows for significant improvements in computational efficiency while addressing complex, high-dimensional design spaces. By applying these ML techniques to both single- and multi-objective optimizations, aimed at minimizing resistance and enhancing seakeeping performance, the proposed framework demonstrates its practical value in hydrodynamic design. This approach provides a scalable and efficient solution, reducing the reliance on high-fidelity simulations while accelerating the optimization process, without substantial modifications to existing toolchains. A design-space dimensionality reduction of approximately 70% is achieved, reducing the design variables from 22 to 7 while retaining 95% of the original geometric variance. Additionally, computational cost reductions of 65% to 98% are observed, compared to using the full design space and high-fidelity simulations only.

Hydrodynamic optimizations of ship hull forms have been carried out employing parametric curves generated by fairness-optimized B-Spline form parameter curves, labeled as F-Spline. Two functionalities of the parametric geometry models are used in the present study: a constrained transformation function to account for hull form variations and a geometric entity used in full parametric hull form design. The present F-Spline based optimization procedure is applied to two distinct hydrodynamic hull form optimizations: the global shape optimization of an ultra-large container ship and the forebody hull form for the hydrodynamic optimization of an LPG carrier. Improvements of ship performance achieved by the proposed F-Spline procedure are demonstrated through numerical experiments and through correlations with experimental data. The ultra-large containership was built and delivered to the ship owner. The present study validates the effectiveness of the proposed hydrodynamic optimization procedure, ushering in process automation and performance improvement in practical ship design practices.

Yin, X.; Lu, Q.; Lu, Y.; Zou, J., and Wan, L., 2021. Hydrodynamic optimization of foreship hull-form using contrastive optimization algorithms. Journal of Coastal Research, 37(5), 1063–1078. Coconut Creek (Florida), ISSN 0749-0208. In this study, a hydrodynamic optimization design of the foreship hull-form for the Series 60 ship is presented in terms of minimum wave-making resistance by using contrastive optimization algorithms. The partially parametric approach was developed to modify the original foreship hull-form. The wave-making resistance as an objective function was obtained by the Rankine source panel method with nonlinear free-surface boundary conditions in which the numerical computation results were validated against available experimental data and found to be in good agreement with the test. Different optimal design methods were proposed based on the minimum wave-making resistance: the nonlinear programming method (NLP), the nondominated sorting genetic algorithm (NSGA-II), and particle swarm optimization (PSO). Through the implementation and integration of the hull-form deformation module, the hydrodynamic module, and the optimization module the hydrodynamic optimization framework can be established subsequently. This module realizes the full automation of ship-shape optimization design and searches for the most efficient target through intelligent optimization algorithm. The hydrodynamic optimization applications for the Series 60 ship indicate that the wave-making resistance is reduced distinctly at various Froude numbers by the contrastive optimization algorithms and each optimized foreship hull-form is smoothing. The present study demonstrates an effective and robust integrated approach for the hydrodynamic optimization of ship design.

Hull geometry modification and reconstruction, optimization algorithms and CFD technique are combined together into what is known as simulation-based design (SBD) techniques, and its essence is the hydrodynamic configuration design driven by global flow optimization. The purpose of this paper is to show how the improvement in the hydrodynamics performance of a Small Waterplane Area Twin Hull (SWATH) can be obtained by solving a shape optimization design problem at different speeds using the SBD technique. In this paper, an example of the technique application for the SWATH hull-form optimization at different speeds is demonstrated. In the procedure, the free-form deformation method is chosen to automatically modify the geometry of submerged pontoon, and the multi-objective particle swarm optimization (MOPSO) algorithm is adopted for exploring the design space. The two objectives functions, the total resistance at two different speeds (11 kn and 15 kn), are assessed by RANS solvers. The optimization results show that the decrease in total resistance is significant for the optimization case at two different speeds (11 kn and 15 kn), with a reduction of about 5.1% and 6.3%, respectively. Meanwhile, the displacement increases 3.7% and the transverse section of submerged pontoon becomes “flower vase” type from ellipse. Finally, dedicated experimental campaigns for optimized model have been carried out to validate the computations and establish the effects of the optimization processes. It shows that the computations of optimization scheme mainly fit the results of model test. The given, practical examples demonstrate the practicability and superiority of the proposed SBD technique for the SWATH multiple speed integrated optimization problem. In addition, the technique can also be applied to other fields of shape optimization design.

At present, the landing of seams and butts on the ship hull surface, which plays the important role in the shipbuilding process, is still carried out based on the designers’ skill and experience. In this paper, a new optimization model and its solution method for the landing of seams and butts (for convenience, seam and butt are simply called seam) on the ship hull surface are proposed in order to improve the shipbuilding efficiency. The minimum manufacturing cost of ship hull shell plates (SHSPs) is the objective function, the rules and requirements for the seam position and the size of a single shell plate are the constraints, and the position and shape of the seam are the design variables. The manufacturing cost consists of the costs of cutting, face cutting, bending, and welding, which are calculated for a seam landing, and the strip method is applied to develop the curved shell plate in the bending cost calculation. We consider a shape optimization problem that the optimal solution is searched among the possible alternatives when the position and shape of the seams are changed and apply the genetic algorithm to the solution of this shape optimization problem. The proposed method is applied to a 4200 DWT bulk carrier and a fishing vessel to find the optimal solutions, and compared with the existing seam landings of the ships and the optimum solutions by Taguchi method. The calculation shows that the manufacturing cost by the proposed method for 4200 DWT bulk carrier is reduced by 5.8% and 4.5%, respectively, compared to the existing seam landing and Taguchi method, and by 6.6% and 4.8% for the fishing vessel, respectively. This shows that the proposed method is more effective than the designers’ experience and Taguchi method.

Increased maritime cargo transportation has necessitated stricter management of emissions from ships. The primary source of this pollution is fuel combustion, which is influenced by factors such as a ship’s added wave resistance. Accurate estimation of this resistance during ship design is crucial for minimizing exhaust emissions. The challenge is that, at the preliminary parametric design stage, only limited geometric data about the ship is available, and the existing methods for estimating added wave resistance cannot be applied. This article presents the application of artificial neural network (ANN) ensembles for estimating added wave resistance based on dimensionless design parameters available at the preliminary design stage, such as the length-to-breadth ratio (L/B), breadth-to-draught ratio (B/T), length-to-draught ratio (L/T), block coefficient (CB), and the Froude number (Fn). Four different ANN ensembles are developed to predict this resistance using both complete sets of design characteristics (i.e., L/B, B/T, CB, and Fn) and incomplete sets, such as L/B, CB, and Fn; B/T, CB, and Fn; and L/T, CB, and Fn. This approach allows for the consideration of CO2 emissions at the parametric design stage when only limited ship dimensions are known. An example in this article demonstrates that minor modifications to typical container ship designs can significantly reduce added wave resistance, resulting in a daily reduction of up to 2.55 tons of CO2 emissions. This reduction is equivalent to the emissions produced by 778 cars per day, highlighting the environmental benefits of optimizing ship design.

With the continuous development of modern ship technology, the design of ships is no longer limited to meeting functional requirements, but also deepens the demand for modelling. Ship exterior modelling not only affects the aesthetic characteristics of the ship, but also influences the ship’s sailing resistance and energy consumption level. Based on the drag coefficient of the ship, this paper explores the influence of different configurations of ship exterior modelling design on the wind resistance of the ship, in order to provide design guidance for modern ship exterior modelling design. To this end, this paper takes the front face design of a certain ship type as an example. Based on the confirmation of the front face design by the client, the windward face of the ship type is divided into three characteristics: the ratio of the front face segmentation width, the inclination angle between the main elevation of the front face and the vertical plane, and the shape of the main elevation of the front face. An orthogonal experimental design for aerodynamic drag simulation is proposed. For the experimental combinations, a ship simulation model is established in SolidWorks, and fluid simulation analysis is conducted using StarCCM + to obtain the drag coefficient at each experimental level as the evaluation criterion.

The paper presents the use of a supervised active learning approach for the solution of a simulation-driven design optimization (SDDO) problem, pertaining to the resistance reduction of a destroyer-type vessel in calm water. The optimization is formulated as a single-objective, single-point problem with both geometrical and operational constraints. The latter also considers seakeeping performance at multiple conditions. A surrogate model is used, based on stochastic radial basis functions with lower confidence bounding, as a supervised active learning approach. Furthermore, a multi-fidelity formulation, leveraging on unsteady Reynolds-averaged Navier–Stokes equations and potential flow solvers, is used in order to reduce the computational cost of the SDDO procedure. Exploring a five-dimensional design space based on free-form deformation under limited computational resources, the optimal configuration achieves a resistance reduction of about 3% at the escape speed and about 6.4% on average over the operational speed range.