Ship structures are subjected to cyclic loading from waves and currents during operation, which can lead to fatigue failure, particularly at locations with structural discontinuities such as welds. Although various fatigue assessment methods have been developed, there is a lack of experimental data and comparative studies for actual ship structure details. This study addresses this limitation by evaluating the fatigue strength of longi-web connections in hull structures using local stress approaches, including hot spot stress, effective notch stress, notch stress intensity factor, and structural stress methods. Finite element analyses were conducted, and the predicted fatigue lives and failure locations were compared with experimental results. Although there are some differences between each method, all methods are valid and reasonable for predicting the primary failure locations and evaluating fatigue life. These findings provide a basis for considering suitable fatigue assessment methods for welded ship structures with respect to joint geometry and failure mechanisms.
In a maritime application for Small Modular Reactors (SMR), Floating Nuclear Power Plants (FNPP) are emerging as a game changer in the offshore industry. The successful development of FNPP is dependent on the concept and design of containment system and its ability to safely shield radiations and protect the SMR system in case of accidents. Unlike land-based nuclear power plants (NPP), marine facilities like FNPP offer a limited footprint for nuclear modules. This limitation causes difficulties in FNPP’s direct application of the concepts of the fourth and fifth barriers including reinforced concrete structures as practiced in land-based NPPs. The concrete shielding systems of land-based NPP need to be replaced with a maritime containment system that can be installed inside FNPP to ensure safe working environments for the crew. The present paper suggests the basic concept of a maritime containment system and provides details on how to install the system inside FNPP, in a manner which is able to shield radiation and protect the reactor system. This paper addresses details on how to comply with regulations on annual radiation dose limits. The main focus is placed on how to arrange the containment system so that the limited footprint may be efficiently utilized. Furthermore, special emphasis is made on implementation of a practically feasible design to shield neutrons, which will not be easily blocked by commonly used metals. The paper also highlights the importance of safely protecting the reactor in accident scenarios to guarantee the survivability of the reactor system with its core reactive substance kept safe.
This review paper provides an overview of simulation-based hydrodynamic design optimization for ship hull forms. It also includes a numerical analysis aimed at accomplish early-stage simulation-based design in terms of hydrodynamic performance. A hydrodynamic module, a hull surface modeling module, and an optimization module are the primary components of this numerical analysis. The hydrodynamic module includes both simple design approaches and high-fidelity numeric tools; these integrated tools are used to evaluate hydrodynamic performances at different design stages. The hull surface modeling module offers a variety of techniques for ship hull surface representation and modification. It is also used to automatically create hull forms or change existing hull forms based on hydrodynamic performance and design constraints. The optimization module includes several optimization algorithms and surrogate models used to determine optimal designs in terms of hydrodynamic performance. Numerical findings indicate that the current tool is well suited for hull form design optimization at the early design stage because it can produce effective optimal designs within a short time.
This paper proposes a novel deep learning model and Edge-AI technology for early detection of anomalies in the main engine system of LNG carriers. The main engine system is a critical component of a ship, and any abnormalities can lead to serious accidents. Conventional anomaly detection methods do not consider the residuals of time-series forecasting and the transferability of the model to multiple ships. The proposed deep learning model consists of two LSTM-based Revin-AutoEncoder models, which utilize the Revin technique to remove non-stationary information and compensate for the residual generated by unstable time-series forecasting. Furthermore, Edge-AI technology is employed to perform model inference without communicating with a central server, enabling fast detection and response to abnormalities, preventing network congestion, and reducing costs. The effectiveness of the proposed method in detecting anomalies in a new LNG carrier with various equipment and systems is experimentally demonstrated, overcoming the challenge of anomaly detection caused by the diverse equipment and systems of a ship. The experimental results showed a maximum recall performance of 0.78. The proposed system show the possibility of the learned model performing early anomaly detection on another ship and is expected to contribute to the development of anomaly detection technology in various industries.
