The latest International Maritime Organization strategies aim to reduce 70% of the CO₂ emissions and 50% of the Greenhouse Gas (GHG) emissions from maritime activities by 2050, compared to 2008 levels. The EU has set up goals to reduce GHG emissions by at least 55% by 2030, compared to 1990, and achieve net-zero GHG emissions by 2050. The UK aims to achieve more than 68% GHG emission reduction by 2030 and net-zero GHG emissions by 2050. There are many solutions under development to tackle the challenge of meeting the latest decarbonization strategies from the IMO, EU, and UK, among which are hydrogen powered marine vessels. This paper presents a life cycle analysis study for hydrogen fuelled vessels by evaluating their performance in terms of environmental friendliness and economic feasibility. The LCA study will consider the gas emissions and costs during the life stages of the ships, including the construction, operation, maintenance, and recycling phases of the selected vessels. The results of the comparisons with the conventional version of the ships (driven by diesel generators) demonstrate the benefits of using hydrogen for marine transportation: over 80% emission reduction and around 60% life cycle cost savings. A sensitivity analysis shows that the prices of fuels and carbon credits can affect the life cycle cost, and recommendations for low H2 price and high carbon credit in the future are provided to attract the industry to adopt the new fuel.

The article aims to design a calm water resistance predictor based on Machine Learning (ML) Tools and develop a systematic series for battery-driven catamaran hullforms. Additionally, employing a machine learning predictor for design optimization through the utilization of a Genetic Algorithm (GA) in an expedited manner. Regression Trees (RTs), Support Vector Machines (SVMs), and Artificial Neural Network (ANN) regression models are applied for dataset training. A hullform optimization was implemented for various catamarans, including dimensional and hull coefficient parameters based on resistance, structural weight reduction, and battery performance improvement. Design distribution based on Lackenby transformation fulfills all of the design space, and sequentially, a novel self-blending method reconstructs new hullforms based on two parents blending. Finally, a machine learning approach was conducted on the generated data of the case study. This study shows that the ANN algorithm correlates well with the measured resistance. Accordingly, by choosing any new design based on owner requirements, GA optimization obtained the final optimum design by using an ML fast resistance calculator. The optimization process was conducted on a 40 m passenger catamaran case study that achieved a 9.5% cost function improvement. Results show that incorporating the ML tool into the GA optimization process accelerates the ship design process.

Artificial Intelligence (AI) is increasingly integrated into ship design workflows, offering enhanced capabilities for hydrodynamic and structural optimization. This review focuses on AI-based methods applied to key design tasks such as hull resistance prediction, structural weight reduction, and performance-driven form optimization. Techniques examined include deep neural networks (DNNs), support vector machines (SVMs), tree-based ensemble models, genetic algorithms (GAs), and surrogate modeling approaches. Comparative analyses from the literature indicate that ensemble tree methods, such as XGBoost, have achieved predictive accuracies up to $R^2$ = 0.995 in speed–power modeling, marginally surpassing DNN performance, while GA-based structural optimization studies have reported weight reductions exceeding 10%. The findings confirm that no single method is universally superior; rather, effectiveness depends on the problem definition, data quality, and computational resources available. Hybrid strategies that combine physics-based modeling with data-driven learning have demonstrated improved generalization, reduced data requirements, and enhanced interpretability. Practical challenges remain, including limited access to open high-fidelity datasets, the computational demands of complex models, and balancing predictive accuracy with explainability. The review concludes that AI should be employed as a complementary toolkit to augment human expertise, with method selection guided by design objectives, constraints, and integration within the broader ship design process.

The consideration of shallow water effects has gained in importance regarding inland operations. The interaction between the keel and the riverbed affects the hydrodynamic characteristics of marine vessels. The highly complex nature of the interference phenomenon in catamarans makes the shallow water problem more complicated as compared to monohulls. Hence, catamarans are very sensitive to speed changes, as well as to other parameters, such as the shallow water effects. This makes the design of catamarans more challenging than their monohull equivalents. At lower Froude numbers, the higher importance of the frictional resistance makes the greater wetted surface of the catamaran a disadvantage. However, at higher speeds, there is the potential to turn their twin hulls into an advantage. This study aims to investigate the wave-making resistance of a zero-carbon fast passenger ferry operating in shallow water. The URANS (unsteady Reynolds-averaged Navier–Stokes) method was employed for resistance simulations. Then, the double-body approach was followed to decompose the residual resistance into viscous pressure and wave-making resistance with the help of the form factors of the vessel calculated at each speed. The characteristics of the separated wave-making resistance components were obtained, covering low, medium, and high speeds. Significant findings have been reported that contribute to the field by providing insight into the resistance components of a fast catamaran operating in shallow waters.

This study presents a comprehensive analysis of the hydrodynamic resistance components, trim, and sinkage characteristics of a high-speed catamaran ferry through computational fluid dynamics (CFD) simulations utilizing the OpenFOAM CFD tool. By systematically introducing each physical effect step by step, this study investigates how free-surface waves, dynamic motions, demihull interaction, and shallow water affect the vessel's hydrodynamic behavior across a range of speeds. OpenFOAM's snappyHexmesh utility is employed for meshing of computational domain, while simpleFoam and interFoam solvers are utilized for doublebody and free-surface Reynolds-averaged Navier–Stokes simulations, respectively. Experimental validation confirms the accuracy of the simulations for catamarans operating in deep waters. Key findings indicate that the demihull interaction's effect is dependent on the vessel speed, demonstrating both positive and negative effects. Furthermore, a reciprocal relationship between demihull interaction and shallow water effects is observed, with each effect amplifying the other. The pressure resistance exhibits a significant increase around critical speeds in shallow water, accompanied by reduced resistance at higher speeds than in deep water. This phenomenon is attributed by changes in wave patterns when approaching the critical speed and augmented dynamic lift, particularly at the stern after critical speed, leading to reduced overall sinkage and trim.