In this paper, the procedure for the strength evaluation of the piping system installed on liquefied natural gas (LNG) carriers is discussed. A procedure that accounts for the ship’s wave load and hull motion acceleration (as well as the deformation due to the thermal expansion and contraction experienced by the hull during seafaring operations) is presented. The load due to the temperature and self-weight of the piping installed on the deck is also considered. Various operating and load conditions of the LNG piping system are analyzed. Stress analysis is performed by combining various conditions of sustained, occasional, and expansion loads. Stress is assessed using finite element analysis based on beam elements that represent the behavior of the piping. The attributes of the piping system components (such as valves, expansion joints, and supports) are represented in the finite element model while CAESAR-II, a commercial software is used for finite element analysis. Component modeling, load assignment, and load combinations are presented to evaluate pipe stresses under various load conditions. An evaluation model is selected for the piping arrangement of LNG and the evaluated stress is compared with the allowable stress defined by the American Society of Mechanical Engineers (ASME).

In order to utilize wave energy, various wave power systems are being actively researched and developed and interest in them is increasing. To maximize the operational efficiency, it is very important to monitor and maintain the fault of components of the system. In recent years, interest in the management cost, high reliability and facility utilization of such systems has increased. In this regard, fault diagnosis technology including fault factor analysis and fault reproduction is drawing attention as an important main technology. Therefore, in this study, to reproduce and monitor the faults of a wave power system, firstly, the failure mode of the system was analyzed using FMEA analysis. Secondly, according to the derived failure mode and effect, the thrust bearing was selected as a target for fault reproduction and a test equipment bench was constructed. Finally, with the vibration data obtained by conducting the tests, the vibration spectrum was analyzed to extract the features of the data for each operating status; the data was classified by applying the three machine learning algorithms: naïve Bayes (NB), k-nearest neighbor (k-NN), and multi-layer perceptron (MLP). The criteria for determining the fault were derived. It is estimated that a more efficient fault diagnosis is possible by using the standard and fault monitoring method of this study.

This study introduced an experimental setup designed to predict collision pressure exerted on a structure due to ice impacts. The primary aim was to forecast the pressure distribution and structural behavior following an ice collision on a laboratory scale. The apparatus comprises a double-pendulum system situated within a cold chamber, facilitating the prediction of collision forces by varying both the collision energy and velocity. Experiments were conducted at collision velocities of 5, 7, and 10 knots, representative of conditions in Arctic regions. By altering these velocities, we measured both the strain experienced by a steel plate and the pressure due to the ice collision. The pressure distribution on the steel plate surface was also recorded by using pressure-sensitive film. This setup effectively captured the pressure distribution across the collision contact area. The measured pressure and force were compared with that of previous studies to determine the validity of the ice collision force measurement. This methodology successfully captures the relationship between the increasing collision speed and force, providing valuable insights into ice-induced forces and structural strain. It offers an effective measurement technique beneficial for designing ice-resistant structures. In conclusion, this paper enhances our understanding of ice–structure interactions, particularly in the domains of collision force and strain metrics.

This study analyzes the impact of different types of random noise applied in Denoising Autoencoder (DAE) training on fault diagnosis performance, with the aim of improving noise removal for vibration time series data. While conventional studies typically train DAEs using Gaussian random noise, such noise does not fully reflect the complex noise patterns observed in real-world industrial environments. Therefore, this study proposes a novel approach that uses high-frequency noise components extracted from actual vibration data as training noise for the DAE. Both Gaussian and high-frequency noise were used to train separate DAE models, and statistical features (mean, RMS, standard deviation, kurtosis, skewness) were extracted from the denoised signals. The fault diagnosis rates were calculated using One-Class Support Vector Machines (OC-SVM) for performance comparison. As a result, the model trained with high-frequency noise achieved a 0.0293 higher average F1-score than the Gaussian-based model. Notably, the fault detection accuracy using the kurtosis feature improved significantly from 26.22% to 99.5%. Furthermore, the proposed method outperformed the conventional denoising technique based on the Wavelet Transform, demonstrating superior noise reduction capability. These findings demonstrate that incorporating real high-frequency components from vibration data into the DAE training process is effective in enhancing both noise removal and fault diagnosis performance.

Sloshing flows of liquid natural gas (LNG) with multi-phase flow characteristics consisting of liquids and gases can affect the load conditions and structural response of cargo containment systems (CCS). The compressible properties of the sloshing flow can limit the maximum pressure, so a multi-phase fluid model is required to represent the sloshing physics. In this study, we identified a suitable numerical model to simulate the sloshing flow and structural strength evaluation based on the inhomogeneous fluid model. The computational fluid dynamics (CFD) is based on a Eulerian domain model, which is in turn based on the constant volume based finite element method (CVFEM) in a commercial Reynolds-averaged Navier–Stokes CFD code (ANSYS CFX). It includes the interphase momentum transfer between the liquids and gasses. The physics for the sloshing assessment were considered to identify the main aspects of the inhomogeneous multiphase model. For numerical analysis of the sloshing, we conducted a sloshing simulation on the experimental data of the model scale to examine the validity of the results. The velocity of the sloshing flow was extended to the real scale and applied to a local two-way fluid structure interaction (FSI) analysis model. Structural strength evaluation of the LNG CCS by sloshing flow was performed by FSI analysis. Through the example of structural response analysis of Mark III type CCS, the results were discussed and effectiveness of the proposed structural response assessment model by sloshing was reviewed.

This study explores the application of numerical analysis and material models to predict ice impact loads on ships and offshore structures operating in polar regions. An explicit finite element analysis (FEA) approach was employed to simulate an ice and steel plate collision experiment conducted in a cold chamber. The pressure and strain history during the ice collision were calculated and compared with the experimental results. Various material model configurations were applied to the FEA to account for the versatile behavior of ice (whether ductile or brittle), its elastic-plastic yield criteria, and its dynamic strain rate dependency. In addition to the standard linear elastic-perfectly plastic and linear elastic-plastic relationships, this study incorporated the Crushable Foam and Drucker–Prager models, based on the specific ice yield criteria. Considering the ice’s strain rate dependency, collision simulations were conducted for each yield criteria model to compute the strain and reaction force of the plate specimens. By comparing the predicted pressures for each material model combination with the pressures from ice collision experiments, our study proposes material models that consider the yielding, damage, and behavioral characteristics of ice. Lastly, our study proposes a combination of ice material properties that can accurately predict collision force.