The International Maritime Organization stipulates that greenhouse gas emissions from ships should be reduced by at least 50% relative to the amount observed in 2008. Consequently, the demand for liquefied natural gas (LNG)-fueled ships has increased significantly. Therefore, an independent type-C cylindrical tank, which is typically applied as an LNG fuel tank, should be investigated. In this study, structural integrity assessments using finite element analysis are performed on C-type LNG fuel tanks for a sea-cleaning vessel. In addition, the applicability of stainless steel and aluminum alloys is evaluated for LNG tank construction. Structural analyses and fatigue limit evaluations, including heat transfer analyses for the tank based on IGC code requirements, are performed, and the results are compared. The results of this study are expected to facilitate the selection of materials used for independent type-C tanks.

This study presents a hybrid machine learning framework combining an artificial neural network and a genetic algorithm to optimize chemical compositions of shielded metal arc weld metals for achieving targeted mechanical properties. First, a neural network model was trained using a large experimental database provided by Dr. Glyn M. Evans, which includes the chemical compositions and mechanical properties of over 950 shielded metal arc weld metals. The neural network model, optimized via Bayesian optimization, demonstrated high predictive accuracy for properties such as yield strength, ultimate tensile strength, and Charpy impact transition temperatures. To enable inverse design, a genetic algorithm-based optimization was applied to the trained neural network model, iteratively exploring the composition space to find optimal elemental combinations that match predefined mechanical property targets. The proposed hybrid approach successfully identified multiple feasible compositions that closely match the desired mechanical behavior, demonstrating the potential of neural network-assisted inverse design in welding alloy development.

This study presents the development and validation of a complex-valued neural network model to predict wave conditions—such as wave direction, height, and period—surrounding floating structures. Accurate wave predictions are crucial for optimizing the design, operation, and maintenance of offshore platforms and floating offshore wind turbines, particularly in the context of digital twins. The proposed methodology leverages motion data obtained through numerical simulations of a floating structure to train the prediction model, enabling it to predict both the amplitude and phase information of the surrounding waves. The model successfully addresses the challenges of representing wave direction data in polar coordinates and capturing phase differences between motion components, which are difficult for traditional real-valued neural networks. The performance of the model was validated through various test cases, with the maximum prediction error found to be less than 10% and most predictions showing an error of less than 5%. Wave direction predictions demonstrated high accuracy, with errors consistently below 2%. While the model was trained using pseudo-measurement data, the results suggest that high-accuracy predictions can be achieved using real-world measurement data. This work contributes to enhancing wave prediction models for floating structures and is expected to improve the safety, performance, and long-term stability of such systems.

The LNG cargo containment system used in membrane-type LNG cargo tanks must have sufficient dynamic strength to withstand the impact of sloshing loads. However, performing direct dynamic nonlinear transient finite element assessments against design sloshing impact loads with different design specifications can be complicated and time-consuming. To address this, it is effective to use linear superposition methods, such as the triangular impulse response function (TIRF) method, to conduct dynamic transient FE assessments of LNG cargo containment systems. However, as LNG cargo containment systems have a high level of nonlinearities in terms of geometry, material, and boundary effects, it is necessary to evaluate the applicability of the TIRF method in advance. This study investigates the dynamic responses of an LNG cargo containment system using the TIRF method and compares the ultimate value of the structural responses and impulses with that obtained using direct dynamic nonlinear transient assessments. Based on a comparison of a series of FE analyses, the study proposes a design for the partial safety factors for calculating the ultimate bending and shear capacities of an LNG cargo containment system, taking into consideration the dynamic impact of sloshing loads using the TIRF method. Finally, the ultimate shear and bending capacities are calculated using the proposed method and compared with those obtained through direct dynamic nonlinear transient assessments. The results show that the proposed method provides conservative estimates against direct nonlinear finite element simulations, with a difference of around 10% for the mean minus two standard deviations. This approach can be practically applied for early basic design purposes in the shipbuilding industry.

Sloshing in liquid storage tanks is a critical phenomenon that affects the stability, performance, and safety of various engineering systems, including fuel tanks, offshore structures, and industrial storage units. The presence of internal structures, such as vertical baffles, significantly influences the natural sloshing frequencies and fluid motion. However, existing theoretical models often rely on simplified assumptions that restrict their capacity to capture the complexities of fluid–structure interactions in baffled tanks. This study integrates the coupled Eulerian–Lagrangian method with the impulse excitation technique to predict natural sloshing frequencies in a rectangular tank with vertical baffles. By analyzing the system’s response to an impulse excitation, we extracted the dominant sloshing frequencies while considering the impact of baffles on fluid dynamics. This computational approach provides a more realistic representation of sloshing phenomena and enables a parametric analysis of how various tank dimensions, fluid properties, and baffle configurations influence sloshing behavior. The findings of this study contribute to the improved design and optimization of liquid storage tanks, ensuring enhanced stability and performance in practical engineering applications. The integration of impulse excitation with the coupled Eulerian–Lagrangian method marks a significant advancement in sloshing analysis, offering a robust framework for understanding and mitigating the effects of sloshing in baffled tanks.