International Journal of Naval Architecture and Ocean Engineering最新文献

In response to the global call for action to reduce CO₂ emissions, operational measures such as speed, route optimization, and hull cleaning play a significant role in the maritime industry. These measures can be implemented immediately without significant investment in both newbuilding ships and existing ships. To make accurate decisions regarding operational measures, reliable and precise models of environmental conditions and effects of hull fouling are required. In this study, a data-driven approach using linear regression was applied to predict shaft power, fuel consumption, and speed after intensive data preparation and feature engineering. First, a shaft power prediction model was developed by combining three independent submodels: the RPM-power model, hull fouling model, and environmental effect model. Subsequently, fuel consumption and speed prediction models were developed based on the shaft power prediction model. Model validation was performed on a 174K LNG carrier, and the results showed good accuracy even in long-term ship operations of more than two years. The mean absolute percentage errors (MAPEs) of the prediction models were 1.60%, 1.70%, and 2.68% for the shaft power, fuel consumption, and speed, respectively. The validated models were applied to two LNG carriers, and satisfactory results were obtained. This study contributes to greenhouse gas (GHS) reduction by providing interpretable, flexible, and accurate models that can help make correct decisions regarding optimal operational measures.

In general, the resistance of a real ship is estimated using an extrapolation method after doing experimental tests or numerical simulations with a model scale ship. Since the only Froude similarity is applied in the model test and simulation, the flow characteristics between the model and real ships could be different due to the inconsistency of Reynolds number. However, in the Computational Fluid Dynamics (CFD), the Froude and Reynolds numbers can be satisfied simultaneously because a fluid with virtual properties can be applied. This study investigated the effect of turbulence models and scales for a flat plate. And then the hydrodynamic feasibility of using a virtual fluid was investigated through numerical analysis. The resistance performance and flow structure of the ship were analysed by applying the virtual fluid, and they were confirmed how well these values and flow characteristics simulate the full-scale with a real fluid. This study shows that the results of a full-scale can be obtained at model scale by applying a virtual fluid instead of full-scale numerical simulations that require more computational resources.

Pipe spool supply in Korean shipbuilding industry consists of a complex supply chain comprising manufacturing and painting vendors. Major Korean shipyards with the most competitive edge have applied various policies and techniques to control the pipe spool supply chain and prevent production delays related to supply delays in pipe spools. Although research has been done on implementing the policies and techniques, there has been a gap on the theoretical consideration of pipe spool supply chain characteristics using a simulation model. Therefore, this study proposes an analytical method based on queueing model to analyze the characteristics of the pipe spool supply chain and also proposes a simulation model based on discrete event simulation (DES) to verify it. In addition, a numerical experiment using DES for the M/G/1 queuing model is conducted to examine the effect of time variability in the spool supply chain. From the more reliable experimental results, control parameters and management insights to manage the supply chain optimality are proposed and the effectiveness of policies that have been implemented by major shipyards is verified.

The present work explores the impact of tip clearance on the mean blade height ratio, inlet tip blade angle, and surface roughness of the inducer. The objective is to find an optimized inducer to limit the secondary flow over the blades, which in turn improves the pump efficiency and reduces the life cycle costs. A numerical framework is developed to investigate efficient operational and geometrical parameters on an inducer's cavitation and non-cavitation presentations. The catalyser functioning is simulated by applying a 3D CFD model, and the results are assessed against empirical data. The results show a reliable agreement with empirical data and suggest that the increment of tip clearance in the mean blade height ratio causes the hydraulic performance and the analytical cavitation number to decline in cavitation and non-cavitation conditions. Moreover, the optimum value of 85^o is found for the inlet tip blade angle, which improves the non-cavitation performance.

With the decreasing availability of sailors, there has been an increasing focus on the development of autonomous ships. Among the various components of autonomous ships, automatic recognition systems that can replace human vision are a crucial area of research. While ongoing studies utilize traditional perception sensors such as RADAR (RAdio Detection And Ranging) and AIS (Automatic Identification System), they have limitations such as blind spots and a restricted detection range. To address these limitations, this paper proposes a new recognition method that utilizes multiple cameras, including electro-optical and infrared radiation cameras, to supplement traditional perception sensors. This method aims to detect maritime obstacles accurately and estimate their dynamic motion using a tracking process. Initially, real-sea images were collected for maritime obstacle detection, and a deep-learning-based detection model was trained on them. The detection results were then employed in an adaptive tracking filter, which allowed the precise motion estimation of the obstacles. Furthermore, to compensate for the limitations of using individual cameras as sensors, this study introduces the simultaneous fusion of tracked data from multiple cameras. This fusion process enhances tracking results in various ways. In field tests using multiple Unmanned Surface Vehicles (USVs), the proposed method successfully converged tracking results within the range of GPS errors. In addition, the fusion of tracked data from multiple cameras significantly improved the tracking results obtained from a single camera.

In this study, physical aspects of a ventilated supercavity behind different cavitator geometries such as the hydrodynamic characteristics, distribution of pressure within the cavity, hysteresis phenomenon, and gas leakage mechanism were qualitatively and quantitatively investigated using experimental and numerical methods. For the simulation and tunnel tests, we employed five cavitators, each with different angles (45°, 60°, 90°, 135°, and a 180° cavitator, commonly referred to as a disk cavitator), all sharing the same diameter. The results revealed that the drag force experienced on the cavitator decreased linearly with an increase in the ventilation rate, and a consistent trend was observed for all test cavitator angles. Through experimental measurements, a universal equation has been derived to predict the drag force exerted on a supercavitating vehicle employing a cavitator. In addition, the pressure distribution inside the supercavity was significantly influenced by the angle of the cavitator. The pressure kept almost unchanged in the first half of supercavity; a slight increase in pressure occurred in the remainder of the supercavity. Twin-vortex gas leakage mode was clearly observed. The distance between the two hollow vortices increased significantly, whereas the incline angle of these vortices and the horizontal line changed insignificantly.

The intelligent recognition of ship geometric features is a prerequisite for enabling computers to automatically generate and deform ship hull surfaces according to requirements, thereby replacing the work of human designers to improve design efficiency. This paper aims to research the recognition of geometric features in three-dimensional ship data using PointNet. To achieve this goal, we first construct two ship point cloud datasets suitable for global feature classification and feature part segmentation of three-dimensional hulls. Subsequently, we conducted recognition capability testing to determine the optimal hyperparameters for identifying ship feature networks. Finally, we employ ship models with non-standard positions to implement data augmentation, enhancing the network's robustness in recognizing the initial positions of ships and achieving rapid cognition of three-dimensional ship geometric features. The findings of this research will provide technical support for ship design based on artificial intelligence technology.