Numerical analysis of a solid oxide fuel cell system integrated with a hybrid desiccant cooling system

Abstract Solid oxide fuel cell systems are advantageous in distributed power generation and are commonly utilized in residential buildings. However, since commercialized small-scale solid oxide fuel cells generally produce hot water using high-temperature exhaust gas, it is difficult to utilize thermal energy in high-temperature areas and during the summer. The exhaust heat from the solid […]

A feasibility study of HFO refrigerants for onboard BOG liquefaction processes

Abstract As the global demand for natural gas continues to increase, the production of liquefied natural gas (LNG) and the demand for LNG carriers are also on the rise. Following the advent of LNG propulsion engines, there has been widespread adoption of systems that use LNG or boil-off gas (BOG) as fuel, and subsequently re-liquefy remaining gas thorough the […]

Enhanced Body-Force Propeller Model for Non-Uniform Inflow Flow and Application to Turning Circle Test of KCS in Calm Water

Abstract An enhanced body-force propeller model is developed to consider propulsion effects without solving the actual propeller geometry in ship maneuvering problems. Application to the KCS turning circle test in calm water (starting at the self-propulsion point) is conducted using the computational fluid dynamics solver, snuMHLFoam, which was developed on OpenFOAM-plus. Based on the original […]

A Simplified Approach for Predicting Bend Radius in HDPE Pipelines during Offshore Installation

Abstract Traditionally, subsea pipelines designed for the transportation of oil, gas, and water are constructed using carbon steel due to its strength, toughness, and ability to operate at temperatures up to 427 °C. However, polyethylene (PE), especially its high-density variant (HDPE), presents advantages such as reduced installation costs, diminished water leakage, and superior corrosion resistance. […]

Investigation of the operation characteristics and optimization of an alkaline water electrolysis system at high temperature and a high current density

Abstract Increasing the operating temperature of water electrolysis is an effective way to improve its performance. When the available region of the operating current density is extended, the stack can generate an increased amount of hydrogen. This can directly contribute to green hydrogen being highly competitive compared to other kinds of hydrogen. In this study, numerical analysis was conducted […]

Application of Empirical Mode Decomposition and Hodrick Prescot filter for the prediction single step and multistep significant wave height with LSTM

Abstract The study discusses the application of the Empirical Model Decomposition (EMD) and Hodrick Prescot (HP) filter for significant wave height prediction. Long Short-Term Memory (LSTM) is applied for sequence predictions in Natural Language Processing (NLP). LSTM can also be modeled to time series forecasting, particularly for cyclic data since LSTM learns from experience to forecast the data. The […]

Prediction of Wave-Induced Ship Motions Based on Integrated Neural Network System and Spatiotemporal Wave-Field Data

Abstract This study introduces an artificial neural network system for ship motion prediction in seaways. To consider the physical characteristics of wave-induced ship motions, neural networks based on a Long Short-Term Memory (LSTM) encoder and decoder, and a convolutional neural network (CNN) are integrated. The LSTM encoder computes the state vector representing the memory effects […]

Composite propeller design optimization for cavitation minimization using deep learning-based objective parameter prediction model

Abstract Recently, composite propellers have attracted attention as a means of reducing cavitation. To maximize cavitation reduction when using a composite propeller, the design of a composite propeller must be optimized. In this study, deep learning-based prediction models for composite propeller design optimization and design optimization procedures based on these models are proposed. The prediction models are trained using […]