International Journal of Naval Architecture and Ocean Engineering最新文献

The interaction of multiple spark-generated bubbles near solid boundary is investigated experimentally. Qualitative study is done with high-speed imaging for bubble shape evolution and PVDF sensor for impact force measurement. Similar-sized bubbles are created synchronously and distance between bubbles or boundary is chosen to be as small as possible. The jet direction of two horizontally aligned bubbles is strongly influenced by proximity parameter (γ) near boundary. The role of inter bubble distance (η) between bubbles and its contribution to intensity of impulsive force is also presented. It is found that strongest impact is recorded for horizontal pair, compared to vertical pair and diagonal pair, for small η values. Three bubbles are arranged with middle one which is smaller, similar or bigger than left and right bubbles. Allocating bigger bubble in the center indeed produces the most destructive impact on boundary among all cases. Moreover, diverse bubble deformation features are witnessed for various combination of dimensionless parameter applied in this study.

The critical aspect in advancing global trade lies in ensuring security across marine trading systems. This research significantly contributes to monitoring ships by assessing operational performance parameters. These parameters are sensed through ship-attached sensors, and further transmitted to the cloud via an Internet of Things (IoT) Module. To optimize the efficiency of this data transfer to the cloud, the proposed work employs a Whale Optimization Algorithm-based Radial Basis Function Neural Network (WOA-RBFNN). The utilization of this algorithm enhances the overall effectiveness of the system. Furthermore, to address security concerns, a 128-bit Two Fish Algorithm-based key management is introduced, providing enhanced data protection. This dual-focus approach not only optimizes the efficiency of data transmission but also fortifies the system against potential cyber threats. These enhancements collectively contribute to the successful and secure transmission of information to the cloud advancing the security and efficiency of marine trading systems.

In this paper, we propose a collision avoidance method based on deep reinforcement learning (DRL) that simultaneously controls the path and speed of a ship. The DRL is actively applied in machine control and artificial intelligence. To verify the proposed method, we applied it to the Imazu problem. It provides benchmark scenarios for collision avoidance. In particular, we compared and analyzed the collision avoidance performance according to the level of learning and various parameters to ensure that the proposed method displays optimal avoidance performance. The results indicated that the proposed method can determine a safe avoidance path for a given situation. Finally, to compare the performance of the proposed method, we compared the collision avoidance method based on the path–speed control of the OS proposed in this study with the collision avoidance method that controls only the path of the OS (Chun et al., 2021). We observed that the proposed method failed in 6 out of 20 scenarios of the Imazu problem when only the path of the OS was controlled. However, it succeeded in collision avoidance in all the 20 scenarios when both path and speed were controlled simultaneously.

The stealth performance of submarines is closely related to their hull forms. In this study, an optimization method based on Deep Reinforcement Learning (DRL) was developed to design submarine hull forms, aimed at maximizing the stealth performance. The DRL optimization technique relied on the decision-making process of an agent for determining actions resulting in changes in the hull form, using stealth performance as the reward. The stealth performance of the submarine was evaluated through a Target Strength (TS) analysis model. Additionally, functional constraints of the examined hull forms were implemented in the optimization process, including geometric constraints related to the hull form and dynamic stability constraints pertaining to the hydrodynamic maneuvering characteristics. The TS of the final optimized hull form was 6.5 dB lower than that of the base model, indicating remarkable stealth performance and improved maneuverability. These results validated the effectiveness of the proposed DRL-based optimization method.

Recently, there has been increasing awareness about greenhouse gases, resulting in considerable efforts to increase the fuel efficiency of ships and reduce the amount of gases they emit. Thus, pre-swirl ducts (PSDs) have been widely adopted in ships due to their high fuel efficiency. However, attaching PSDs induces a change in the fatigue characteristics of ship structures, as evidenced by the reported crack in the rudder of a ship with an attached PSD. Nevertheless, the arrangement of a ship’s rudder, which is a crucial factor in a rudder’s fatigue characteristics, continues to rely on the empirical rule without any consideration of fatigue characteristics. This study investigated a propeller–rudder interaction distance ensuring fatigue stability with the PSD attachment and developed a procedure for designing it considering fatigue characteristics. In the developed procedure, the fatigue characteristics of rudders due to the propeller wake were obtained through frequency domain finite element method (FEM) analysis utilizing computational fluid dynamics (CFD) analysis results of a rudder’s surface pressure fluctuations. In addition, the potential for fatigue failures in ship rudders was evaluated by employing damage factors. A comparative analysis of rudder fatigue characteristics considering the presence of PSDs revealed that attaching them increased the stresses in the rudder. Furthermore, the analysis results demonstrated that PSD attachment can result in the fatigue failure of rudders. Finally, the propeller–rudder interaction distance of ships with an attached PSD was newly proposed using the developed procedure. The proposed propeller–rudder interaction distance is expected to enable the design of appropriate rudder arrangements for ships with attached PSDs while ensuring fatigue stability.

The digital transformation of ship systems requires the coding and management of large amounts of Input/Output (IO) data generated by various pieces of equipment during ship operation. In this study, we investigated a method that recognizes the text of the IO description of a ship to automatically code IO data. Accordingly, the characteristics of the IO descriptions were extracted using Term Frequency-Inverse Document Frequency (TF–IDF) and word embedding, and machine learning techniques such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) and deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and bidirectional LSTM (BiLSTM) were used to classify them into codes. Through the application of different text preprocessing techniques based on the unique characteristics of the data, the performances of the algorithms improved; the experimental results showed an accuracy of up to 91%, with an average improvement in accuracy of 5% for each algorithm.

A first-order analytical theory on a truncated and surface-piercing vertical circular cylinder with a circular plate mounted at the bottom of the cylinder was generalized to solve the linear wave diffraction and radiation problems based on potential flow and the hypothesis of small wave and motion amplitudes. The domain was decomposed, and the linearized velocity potentials were derived in each subdomain and matched on each subdomain interface employing pressure and normal velocity continuity. The linear hydrodynamic loads obtained with the analytical method were compared with the results of linear boundary element method (BEM) solvers. Dedicated experimental campaigns were performed on fixed models with regular waves (diffraction) and then on models oscillating in surge, heave, and pitch motions without incident waves (radiation). The analytical method describes well the wave excitation loads from the experiments for small wave steepness ($H/\lambda =0.02$). The predictions of added mass and damping are shown to be applicable to the surge motion, only for small Keulegan-Carpenter numbers ($KC<1$). On the other hand, the analytically predicted radiated waves demonstrate satisfactory agreement with experiments for all motions.

Active research is underway to utilize various sensors for situational awareness, but validating these studies requires real sensor data, which is challenging to acquire due to the time and space constraints inherent in ships. Additionally, the diverse formats of sensor data and the need for postprocessing pose further hurdles. By developing a ship navigation simulator that faithfully replicates real-world environments, these limitations can be overcome, and ample virtual sensor data can be generated through immersive simulations. In this study, we propose a method for implementing such a simulator. A life-sized terrain model was placed within a virtual reality environment, representing maritime environments, including port structures. To simulate ship motions according to maritime weather conditions, we applied various laws of physics within virtual reality. Finally, to provide validation data for critical navigation technologies, such as collision avoidance and ship detection, we generated a virtual automatic identification system and camera data. We implemented and operated a ship navigation simulator using this approach, and simulations conducted across various environments generated real-time sensor data efficiently, affirming the effectiveness of the proposed method.