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The main objective of this article is to describe an innovative methodology for the hydrodynamic optimization of a ship bulbous bow which considers multiple operating conditions. The proposed method is more practical and effective than the traditional optimization process, which is only based on contractually specified design condition. Parametric...
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... In order to design a product using computational methods, the product needs to be represented in a way that a computer can understand. For ship design, the two most popular modes are parameterized vectors [1,[4][5][6][7][8][9][10][11][12], and free-form deformation techniques [13][14][15][16][17][18]. The benefit of using parameterized design representations for a hull is that the design is defined by a set of tunable parameters that both human designers and computers can interpret. ...
... These potential flow solvers input the 3D geometry of a hull and provide estimates of drag at typical operating speeds of a hull. The third method for creating a fast prediction of drag is to build a small dataset of drag measures, to train a neural network to predict drag from a hull's design representation [5,6,9,[13][14][15][16]19,20,34,35]. ...
... These two measures of volume affect the ability of ships to generate revenue through the shipment of cargo. The two volume measures are calculated with Equations (8) and (9). With the intention of maximizing the volume metrics in hull generation, the volume measures are multiplied by −1. ...
Ship design is a years-long process that requires balancing complex design trade-offs to create a ship that is efficient and effective. Finding new ways to improve the ship design process could lead to significant cost savings in the time and effort required to design a ship, as well as cost savings in the procurement and operation of a ship. One promising technology is generative artificial intelligence, which has been shown to reduce design cycle times and create novel, high-performing designs. In a literature review, generative artificial intelligence was shown to generate ship hulls; however, ship design is particularly difficult, as the hull of a ship requires the consideration of many objectives. This paper presents a study on the generation of parametric ship hull designs using a parametric diffusion model that considers multiple objectives and constraints for hulls. This denoising diffusion probabilistic model (DDPM) generates the tabular parametric design vectors of a ship hull, which are then constructed into a point cloud and mesh for performance evaluation. In addition to a tabular DDPM, this paper details adding guidance to improve the quality of the generated parametric ship hull designs. By leveraging a classifier to guide sample generation, the DDPM produced feasible parametric ship hulls that maintained the coverage of the initial training dataset of ship hulls with a 99.5% rate, a 149× improvement over random sampling of the design vector parameters across the design space. Parametric ship hulls produced using performance guidance saw an average 91.4% reduction in wave drag coefficients and an average 47.9× relative increase in the total displaced volume of the hulls compared to the mean performance of the hulls in the training dataset. The use of a DDPM to generate parametric ship hulls can reduce design times by generating high-performing hull designs for future analysis. These generated hulls have low drag and high volume, which can reduce the cost of operating a ship and increase its potential to generate revenue.
... A dataset of ship hulls will need a design representation that is comprehensive enough to cover the broad spectrum of traditional ship hull forms. A literature review has shown multiple representations for complex designs, including graphs [2,7], images [9,12], parameterized vectors [1,8,10,[13][14][15][16][17][18][19][20][21], and free form deformation techniques [22][23][24][25][26]. The most common representation found for ship hulls was vectored parameterization. ...
Machine learning has recently made significant strides in reducing design cycle time for complex products. Ship design, which currently involves years-long cycles and small batch production, could greatly benefit from these advancements. By developing a machine learning tool for ship design that learns from the design of many different types of ships, trade-offs in ship design could be identified and optimized. However, the lack of publicly available ship design datasets currently limits the potential for leveraging machine learning in generalized ship design. To address this gap, this paper presents a large dataset of 30,000 ship hulls, each with design and functional performance information, including parameterization, mesh, point-cloud, and image representations, as well as 32 hydrodynamic drag measures under different operating conditions. The dataset is structured to allow human input and is also designed for computational methods. Additionally, the paper introduces a set of 12 ship hulls from publicly available CAD repositories to showcase the proposed parameterization’s ability to accurately reconstruct existing hulls. A surrogate model was developed to predict the 32 wave drag coefficients, which was then implemented in a genetic algorithm case study to reduce the total drag of a hull by 60% while maintaining the shape of the hull’s cross section and the length of the parallel midbody. Our work provides a comprehensive dataset and application examples for other researchers to use in advancing data-driven ship design.
... The major goal of a paper presented by [9] is to provide a novel way for optimizing the hydrodynamics of a ship's bulbous bow. The suggested technique outperforms the existing optimization procedure in terms of practicality and efficiency. ...
The computational fluid dynamics (CFD) field has been developing rapidly in recent years. Such a development is attributed to the enormous growth of computational power and availability of advanced methods for solving problems in CFD. The literature on CFD is vast and somewhat scattered. In order to address the problem, this paper presents a comprehensive survey of available computational methods in the field. It covers a fascinating use of differential equation techniques to solve some important fluid flow problems that lead to advances within the field of fluid dynamics.
... A dataset of ship hulls will need a design representation that is comprehensive enough to cover the broad spectrum of traditional ship hull forms. A literature review has shown multiple representations for complex designs, including graphs [7,2], images [12,9], parameterized vectors [8,1,13,14,10,15,16,17,18,19,20,21], and free form deformation techniques [22,23,24,25,26]. The most common representation found for ship hulls was vectored parameterization. ...
... These potential flow solvers input the 3D geometry of a hull and provide accurate measures of drag at typical operating speeds of a hull. As seen in the literature review, the balance of computational speed and accurate results make potential flow solvers great candidates for both optimization and machine learning methods for ship hull design [23,24,16,15,25,10,19,14] Among the available solvers, the Michell Integral was chosen as the simulation for hulls in this dataset. ...
Machine learning has recently made significant strides in reducing design cycle time for complex products. Ship design, which currently involves years long cycles and small batch production, could greatly benefit from these advancements. By developing a machine learning tool for ship design that learns from the design of many different types of ships, tradeoffs in ship design could be identified and optimized. However, the lack of publicly available ship design datasets currently limits the potential for leveraging machine learning in generalized ship design. To address this gap, this paper presents a large dataset of thirty thousand ship hulls, each with design and functional performance information, including parameterization, mesh, point cloud, and image representations, as well as thirty two hydrodynamic drag measures under different operating conditions. The dataset is structured to allow human input and is also designed for computational methods. Additionally, the paper introduces a set of twelve ship hulls from publicly available CAD repositories to showcase the proposed parameterizations ability to accurately reconstruct existing hulls. A surrogate model was developed to predict the thirty two wave drag coefficients, which was then implemented in a genetic algorithm case study to reduce the total drag of a hull by sixty percent while maintaining the shape of the hulls cross section and the length of the parallel midbody. Our work provides a comprehensive dataset and application examples for other researchers to use in advancing data driven ship design.
... In (Matulja and Dejhalla, 2013), the hull form, including the bulbous bow, has been optimized by minimizing the wave resistance, while Li et al. (2014) optimized the bow and stern shape, achieving a 5% reduction in resistance and improving the wake field. Furthermore, the optimization procedures have been performed to find the optimal bow shape in different cases; the laden and ballast condition of a tanker ship (Legović and Dejhalla, 2015), a container ship in different operating conditions (Lu et al., 2016) and waves , the added resistance in case of KVLCC2 tanker (Bolbot and Papanikolaou, 2016), a fishing vessel coupling CFD and design of experiment (DoE) (Hong et al., 2017b) and strength assessment of the corner bracket connection (Silva-Campillo et al., 2022). By applying a combination of several machine learning methods (Mittendorf et al., 2022), the selection of a suitable hull operating from a dataset in weather conditions can be more accurate and faster (Islam et al., 2022) than using only CFD techniques. ...
This paper presents a comprehensive review of the current regulations and the various technologies as well as the decision support methods for each technology the maritime industry considers to ensure the fleet's sustainability. It covers the period between 2010 and 2022, emphasizing the last four years. It shows the impact of each technology on the reduction of ship resistance and the energy required on board, affecting the amount of fuel consumption and avoiding the transportation of harmful species around the world to achieve a smooth transition towards green shipping by improving the fleet's energy efficiency and achieving the goals of the 2050 plan. The paper covers five main topics: hull design, propulsion systems, new clean fuels and treatment systems, power systems and ship operation; and each topic has different technologies included. This study's findings contribute to mapping the scientific knowledge of each technology in the maritime field, identifying relevant topic areas, visualising the links between the topics, and recognising research gaps and opportunities. This review helps to present holistic approaches in future research supporting the cooperation between maritime industry stake-holders to provide more realistic solutions toward sustainability.
... Several applications of simulation-based design optimization methods applied to ship hull were presented in the last years. Mainly based on BEM calculations of wave-making resistance, Peri et al. (2001), Campana et al. (2006), Serani et al. (2016Serani et al. ( , 2022Serani et al. ( , 2023, Diez et al. (2015), Kim et al. (2011), Lu et al. (2016b Tezzele et al. (2018) proposed ship (local) hull form optimizations by combining surrogate models and space dimensionality reduction to realize computational efficient design frameworks as an evolution of design methods based on simplified estimation of ship resistance (Chang et al., 2016;Percival et al., 2001). Multi-objective problems, adding seakeeping performance to pure resistance minimization were discussed in Kim et al. (2010) and several approaches to handle the complex hull shape were proposed in Kostas et al. (2015), Harries (1998), Coppedé et al. (2018), Kim and Yang (2010), Shen et al. (2013) and Cheng et al. (2018). ...
The design of pumpjet propulsors (PJP) is addressed through a simulation-based design optimization approach built on a parametric description of the main geometrical characteristics of the system, a RANSE solver with mixing plane interface capabilities and a genetic algorithm. Both Rotor/Stator (PJP-R/S) and Stator/Rotor (PJP-S/R) configurations are considered and optimal designs from a multi-objective optimization process aimed at maximizing the propulsive efficiency at the lowest possible cavitation inception index are compared to a reference ducted propeller with decelerating nozzle (Gaggero et al., 2012), which served as baseline during the design. Detached Eddy Simulations (DES) were finally carried out to highlight, in addition to the performance improvements provided by the PJPs, also the influence of the rotor/stator/nozzle interactions on the vortical structures shed by the propulsor.
... As has already been mentioned, slow steaming lengthens journeys; nonetheless, "a 10% decline in speed may result in a total average emissions reduction of 19% [158]". Slow steaming, however, because it runs at part load for extended periods of time, may potentially harm the engine [158,159]. ...
With stricter IMO regulations on CO2 taking effect in 2023 and ambitious goals to reduce carbon intensity by 2030, the maritime industry is scrambling to clean up its act. Conventional methods and equipment are now being reevaluated, upgraded or completely replaced. The difference between a short-term fix and a long-term sustainable option is how flexible vessels will be to use new energy sources or technology as they become viable. The review discusses the recent literature on renewable energy sources, technical and operational strategies for new and existing ships, technology maturity, and alternative fuels. It is found that the IMO's targets can be met by combining two or three technologies, or via a radical technology shift which can provide innovative, high-efficiency solutions from an environmental and economic standpoint. It has also been noted that policies and enforcement are essential management instruments for mitigating the unfavourable environmental effects of marine transportation and directing the maritime industry toward sustainability on a regional, national, and international scale.
... Bow and ship modeling are designed by using Maxsurf modeler while the resistance will be calculated by using computational fluid dynamics (CFD) Numeca. The CFD analysis is increasingly being used to analyze the optimal hull shape by various methods [12][13][14][15][16]. The result of this method is practically used in the preliminary design phase to predict the ship resistance and engine power requirement [17,18]. ...
Using a bulbous bow on the ship is the most common alternative way used to reduce resistance and fuel consumption. Some developments are created in terms of bow shapes to obtain the minimum shipping cost. The purpose of this study is to compare the total resistance (Rt) and specific fuel oil consumption (SFOC) in an 8000 DWT oil tanker by modifying the existing moor deep ram bow to axe bow using computational fluid dynamics (CFD) and empirical Holtrop method. Based on the results, the total resistance with a moor deep ram bow design at a service speed of 12 knots is 230,8 kN, while axe bow is 221,5 kN. This is directly proportional to the fuel consumption where by using axe bow, the ship will consume 83.64 tons in a trip of 1912 nautical miles. In contrast, with existing moor deep ram bow, the fuel consumption is just a slight 0.1 % higher than axe bow and still showing a competitive performance as it is generally used in cargo vessel.
... Depending on the cost function to be minimized, either potential flow solver or viscous flow RANS solver is employed. Potential flow solver for the evaluation of wavemaking resistance has been widely used for hull form optimization studies [1][2][3][4]6,7,[10][11][12][13][15][16][17][18][19][20][21][23][24][25]27]. From the wavemaking resistance coefficient obtained from potential flow solver, the total resistance coefficient can be calculated by 1 ...
... . Here, the frictional resistance coefficient is calculated using the ITTC friction line. The form factor 1 can be obtained in various ways: empirical formula [12,13], model test results [11,19], double body solution using RANS solver [27]. In spite of the advantage of shorter computational time, the accuracy issue due to negligence of viscosity makes potential flow solver less popular nowadays. ...
... The PSO is a derivative-free, probabilistic optimization method and has been adopted by Kim et al. [17], Huang and Yang [18], Serani et al. [20], and Yu et al. [21]. The hull form SBD studies with GA are Dejhalla et al. [2], Campana et al. [8], Tahara et al. [9], Biliotti et al. [10], Zhang [12], Lu et al. [19], Cheng et al. [23], Lu et al. [24], Zhang and Kim [25], and Miao et al. [26]. ...
In this study, the hull form optimization process to minimize resistance of KCS (KRISO containership) at is described. The bow hull form of KCS was modified by varying such design parameters as sectional area curve (SAC), section shape, bulb breadth, and bulb height using multiple parametric modification curves devised by the authors. The resistance performances of modified hull forms were analysed by the viscous flow Reynolds-Averaged Navier-Stokes (RANS) solver of WAVIS ver.2.2. With a view to saving computational time during iterative analyses in the optimization process, the sinkage and trim were set to the fixed values which had been obtained for the original hull form with free condition. The validity of such constant sinkage/trim was then verified by conducting analysis for the optimal hull form with free condition. Optimization to minimize the cost function of the total resistance coefficient of model was performed by sequential quadratic programming (SQP), which is one of the gradient-based local optimization methods. Utilization of parallel computing led to the simultaneous calculation of the gradient, thereby speeding up the whole optimization process. At the design speed of 24 knots, the optimal hull yielded reduction by 1.8%, which is extrapolated to 3.1% reduction of effective power in full scale.
... Additionally, the Rankine source panel method was applied to evaluate the added waves' resistance acting on the hull of a Ro-Ro ship. The results showed that added wave resistance was clearly reduced and the wave bow profile became gentle in the ship design with optimized lines of the bulbous bow [23]. Others studies also used the Rankine source panel method; for example, Lu et al. (2016) presented a study of an innovative method for the optimization of the hydrodynamic performance of a bulbous bow while considering different conditions [24]. ...
... The results showed that added wave resistance was clearly reduced and the wave bow profile became gentle in the ship design with optimized lines of the bulbous bow [23]. Others studies also used the Rankine source panel method; for example, Lu et al. (2016) presented a study of an innovative method for the optimization of the hydrodynamic performance of a bulbous bow while considering different conditions [24]. Zhang et al. (2017) presented research on the optimal bulbous bow shape based on the newly improved PSO algorithm. ...
In this study, the effect of bow shape on resistance acting on a hull in regular head waves was investigated by applying a commercial Computational Fluid Dynamics (CFD) code. For this purpose, the hydrodynamic performance as well as the resistance of ships with blunt and bulbous bows were simulated. By analyzing the obtained CFD simulation results, the effects of the bow shape on the hydrodynamic performance and resistance of the ships were found. A new bulbous bow shape with drastically reduced added resistance acting on the hull in waves is proposed. Finally, the obtained CFD results for the hydrodynamic performance of ships are presented.
