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The construction accuracy of block hull plates is the key to improving the non-allowance construction rate of ships, and the accuracy inspection during its forming process provides a data basis for ensuring construction accuracy. The existing accuracy measurement of block hull plates relying on total stations still has problems such as low detection efficiency and poor timeliness. Therefore, a rapid reconstruction method for hull block plates based on the point cloud characteristics of hull curved plates is proposed. Firstly, based on geometric attributes, a method for extracting the normal vectors of the boundary points of the curved plate point cloud is proposed, and then a curved plate point cloud splicing method based on the node interpolation algorithm is created. Finally, the rapid reconstruction of the side and stern block hull plates of a bulk carrier is taken as an example to verify the effectiveness of the proposed method.

Optimization of ship hull form in the ship design process aims to reduce ship resistance, which is crucial for economic efficiency and meets the International Maritime Organization (IMO) requirement for emission reduction and energy saving. This paper investigates the impact of the longitudinal centre of buoyancy (LCB) position on the resistance of a cargo ship 4600DWT using the RANSE (Reynolds – averaged Navier – Stokes equation) method. The ship hull form was modified using the Lackenby method. The obtained numerical results indicate that the LCB position significantly affects ship resistance. The physical phenomenon of the change in ship resistance is also explained through the analysis of the differences in the flow field around the ship’s hull form with modified LCB positions.

This paper presents a review of the different methods and techniques used to optimize ship hulls over the last six years (2017–2022). This review shows the different percentages of reduction in ship resistance, and thus in the fuel consumption, to improve ships’ energy efficiency, towards achieving the goal of maritime decarbonization. Operational research and machine learning are the common decision support methods and techniques used to find the optimal solution. This paper covers four research areas to improve ship hulls, including hull form, hull structure, hull cleaning and hull lubrication. In each area of research, several computer programs are used, depending on the study’s complexity and objective. It has been found that no specific method is considered the optimum, while the combination of several methods can achieve more accurate results. Most of the research work is focused on the concept stage of ship design, while research on operational conditions has recently taken place, achieving an improvement in energy efficiency. The finding of this study contributes to mapping the scientific knowledge of each technology used in ship hulls, identifying relevant topic areas, and recognizing research gaps and opportunities. It also helps to present holistic approaches in future research, supporting more realistic solutions towards sustainability.

The ship hull form optimization using the Computational Fluid Dynamics (CFD) method is increasingly employed in the early design of a ship, as an optimal ship hull form can obtain good hydrodynamics. However, it is time-consuming due to its many CFD simulations for the optimization. This paper presents a ship hull form optimization loop using the surrogate model, deep belief network (DBN), to reduce the wave-making resistance of the Wigley ship. The prediction performance of the wave-making resistance of the Wigley ship using the DBN method is discussed and compared with the traditional surrogate models found in this study. The results show that the resistance obtained using the deep belief network algorithm is superior to that obtained using the typical surrogate models. Then, a ship hull form optimization framework is built by integrating the Free From Deformation, non-linear programming by quadratic Lagrangian and deep belief network algorithms. The optimization results show that the deep belief network-based ship hull form optimization loop can be used to optimize the Wigley ship. The study presented in this paper could provide a deep learning algorithm for the ship design optimization.

Most species arriving from a donor to a recipient area do not succeed in establishing long-lasting self-sustaining populations. However, successful introductions are far better documented than those that failed, especially those occurring before or near the advent of the Linnaean binomial nomenclature. We report here an introduction from the mid-18th century (possibly in 1750 or 1751) of an exotic mussel transported as fouling on ship hulls from the western coast of Morocco (Atlantic Ocean) to the port of Marseilles (Mediterranean Sea). The exotic mussel, which survived several years, has been identified as probably being the brown mussel, Perna perna, a species with warm-water affinities, which much later became invasive in several areas of the world ocean. The documents of the 18th and early 19th century, which mentioned the event, held ‘the curious’ and ‘amateurs’, who harvested the mussels, responsible for its extirpation. More realistically, it is hypothesised that the mussel population did not survive the return of severe cold weather conditions, after a few relatively mild decades, in the context of the Little Ice Age (LIA). These conclusions were deduced from historical data and are therefore open to discussion.

Because of EEDI regulations for ships and SDGs, it is of vital importance to develop ships with good propulsion performance, not only in still water, but also in waves. To predict the propulsion and seakeeping performance in waves, and in particular ship motions and added resistance, various theoretical calculation methods and CFD codes have been developed. To validate these estimation methods, experimental data on details of the flow induced by the ship disturbance are required. To meet this demand, the authors developed an innovative method for measuring and analyzing the spatial pressure distribution over the ship-hull surface using a large number of Fiber Bragg Grating (FBG) pressure sensors. However, pressure measurements with the conventional FBG pressure sensors are largely influenced by the difference in the temperature between water and air, referred to as the temperature interference. To resolve this issue, the authors developed a new FBG pressure sensor that incorporates three improvements and is significantly less influenced by temperature interference. Its performance was confirmed through comparisons with measured pressures obtained using existing strain-type and conventional FBG pressure sensors. Furthermore, the effects of different materials used for manufacturing ship models on the pressure measurement were investigated experimentally in towing tank tests. Finally, an experimental study was conducted to determine which of the three improvements in the latest FBG pressure sensor is essential for reducing the temperature interference.

We proposed a GAN-based method to generate a ship hull form. Unlike mathematical hull forms that require geometrical parameters to generate ship hull forms, the proposed method requires desirable ship performance parameters, i.e., the drag coefficient and tonnage. The objective of this study is to demonstrate the feasibility of generating hull geometries directly from performance specifications, without relying on explicit geometrical inputs. To achieve this, we implemented a conditional Wasserstein GAN with gradient penalty (cWGAN-GP) framework. The generator learns to synthesize hull geometries conditioned on target performance values, while the discriminator is trained to distinguish real hull forms from generated ones. The GAN model was trained using a ship hull form dataset generated using the generalized Wigley hull form. The proposed method was evaluated through numerical experiments and successfully generated ship data with small errors: less than 0.08 in mean average percentage error.

Information about the flow field around the ship may enable the designers to improve ship hull form concerning ship hydrodynamics. The flow around the ship can be obtained through model tests in towing tank and simulations. However, the experimental method provides the most reliable data about flow around the ship, but this method is too expensive and time-consuming. Currently, the RANSE (Reynolds – averaged Navier – Stokes equation) method has been widely employed in predicting the ship resistance and flow around the ship in the ship design process due to it provides relatively accurate results, savings in time and money compared to the experimental method. Therefore, this paper deals with the numerical results of the study flow around the 4600DWT cargo ship, which operate on Vietnam’s river-sea routes in calm water condition at different ship speeds using the RANSE method. The effect of ship speed on ship resistance components, wave pattern, wave profile along the ship, distribution of dynamic pressure, and wall shear stress on the ship’s hull surface and nominal wake field are presented and analysed in the paper.

The direct optimization of ship hull designs using deep learning algorithms is increasingly expected, as it proposes optimization directions for designers almost instantaneously, without relying on complex, time-consuming, and expensive hydrodynamic simulations. In this study, we proposed a GAN-based 3D ship hull design optimization method. We eliminated the dependence on hydrodynamic simulations by training a separate model to predict ship performance indicators. Instead of a standard discriminator, we applied a relativistic average discriminator to obtain better feedback regarding the anomalous designs. We add two new loss functions for the generator: one restricts design variability, and the other sets improvement targets using feedback from the performance estimation model. In addition, we propose a new training strategy to improve learning effectiveness and avoid instability during training. The experimental results show that our model can optimize the form factor by 5.251% while limiting the deterioration of other indicators and the variability of the ship hull design.

Mesh generation is a critical preprocessing step in Computational Fluid Dynamics. In ship hydrodynamics, existing mesh generation methods lack adaptability to complex hull surface geometries, necessitating repeated optimization. To address these issues, a new hybrid mesh generation strategy was proposed, integrating Non-Uniform Rational B-Spline surface interpolation, advancing front technique, hull surface curvature features, and mesh quality evaluation parameters. Firstly, the ship hull surface was partitioned into multiple regions, and each region was assigned a specific mesh type. Subsequently, the adaptively sized mesh was generated based on local curvature variations. Finally, the angle skewness was employed as an objective function to improve the mesh quality. In addition, considering the actual ship model as an example, the mesh generated by our method and conventional Laplacian smoothing method were used to perform first-order potential flow simulations, and the results were compared against the convergence values. The results indicated that our method has lower root mean square errors in computing the total non-viscous force, steady drift force and ship hull free floating Response Amplitude Operator. This method is applicable to numerical simulations of the ship potential flow, providing high-quality hull meshes for hydrodynamic analysis.