Mokpo National Maritime University

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries.

The performance of the turbocharger nozzle ring is a key factor in the overall operation of the main engine of the ship. Minimizing failure and damage caused by high exhaust gas temperature and pressure is essential. As a first step toward improving turbocharger safety, this study performed 3D scanning of an aged nozzle ring to obtain its precise geometry and developed a corresponding numerical model. The boundary conditions of the numerical model were defined by the exhaust gas temperature and pressure at various engine output loads. Structural safety was assessed using static structural and stress-life fatigue analyses. A sharp increase in maximum equivalent stress and strain was observed at output loads of 85% and higher. At 25% load, the maximum fatigue life indicated 1.76 × 108 cycles, while at 100% load, the maximum damage index reached 1. A field performance test conducted at 85% of the main engine’s output load revealed severe damage under high-load conditions. Specifically, damage occurred at the contact area between the outer hoop and the tip of the blade’s trailing edge. This observed damage pattern closely aligned with the results predicted by the fatigue life analysis. The validity of the present study was confirmed through a comparative analysis of the fatigue life predictions and the field test results.

ABSTRACT In this paper, the vehicle's dynamical behaviour is first described through a bifurcation analysis to give some insights for its robust control synthesis. Then a novel adaptive fractional-order sliding mode controller (AFOSMC) is designed to effectively control the uncertain supercavitating vehicle against payload changes, nonlinear planing force and external disturbances. The control scheme utilises the fractional calculus and the adaptive law for the sliding mode control (SMC) to provide active control actions. The fractional calculus can guarantee more flexibility and more freedom for tuning active control synthesis than the integer-order counterpart. The adaptation law has been presented to directly handle the payload change effects. The dynamic performance of the proposed controller is verified through extensive simulation results, which demonstrate faster responses than existing integer order controllers. Furthermore, the fractional order controller shows its effectiveness in dealing with external disturbance, when eliminating most of the uncertainty effect.

This research develops a performance evaluation model of humans in avoiding ship collision situations compared to the AI ship collision avoidance system (CAS). A human, the remote operator (RO) of Maritime Autonomous Surface Ships (MASS), ought to make compact collision avoidance (CA) decisions to secure safety and efficiency during remote operations. Hence, evaluating RO’s CA performance is important; however, research on developing evaluation methods concentrates merely on evaluating trainees based on instructor’s guidelines, while artificial intelligence (AI) makes CA decisions through parameter-based calculation. Therefore, this research proposes a CA performance evaluation model for RO of MASS based on CAS intervening of RO trainee’s simulation training in ship collision avoidance. In each time interval, the RO’s decisions showed divergent behaviors under given situational conditions compared to the CAS’s behaviors in equivalent situations. Findings denote the benefits of CAS intervention methods and RO performance evaluation model based on the proposed performance features.

Gas turbine engines at sea, characterized by nonlinear behavior and parameter variations due to dynamic marine environments, pose challenges for precise speed control. The focus of this study was a COGAG system with four LM-2500 gas turbines. A third-order model with time delay was derived at three operating points using commissioning data to capture the engines’ inherent characteristics. The cascade controller design employs a real-coded genetic algorithm–PID (R-PID) controller, optimizing PID parameters for each model. Simulations revealed that the R-PID controllers, optimized for robustness, show Nyquist path stability, maintaining the furthest distance from the critical point (−1, j0). The smallest sensitivity function Ms (maximum sensitivity) values and minimal changes in Ms for uncertain plants confirm robustness against uncertainties. Comparing transient responses, the R-PID controller outperforms traditional methods like IMC and Sadeghi in total variation in control input, settling time, overshoot, and ITAE, despite a slightly slower rise time. However, controllers designed for specific operating points show decreased performance when applied beyond those points, with increased rise time, settling time, and overshoot, highlighting the need for operating-point-specific designs to ensure optimal performance. This research underscores the importance of tailored controller design for effective gas turbine engine management in marine applications.

A complex number [Formula: see text] is said to be non-free if the subgroup of [Formula: see text] generated by [Formula: see text] is not a free group of rank 2. In this case, the number [Formula: see text] is called a relation number, and it has been a long standing problem to determine the relation numbers. In this paper, we characterize the relation numbers by establishing the equivalence between [Formula: see text] being a relation number and [Formula: see text] being a root of a ‘generalized Chebyshev polynomial’. The generalized Chebyshev polynomials of degree [Formula: see text] are given by a sequence of [Formula: see text] integers [Formula: see text] using the usual recursive formula, and thereby can be studied systematically using continuants and continued fractions. Such formulation, then, enables us to prove that, the question whether a given number [Formula: see text] is a relation number of [Formula: see text]-degree [Formula: see text] can be answered by checking only finitely many generalized Chebyshev polynomials. Based on these theorems, we design an algorithm deciding any given number is a relation number with minimal degree [Formula: see text]. With its computer implementation we provide a few sample examples, with a particular emphasis on the well-known conjecture that every rational number in the interval [Formula: see text] is a relation number.