This paper proposes a simulation-based method to estimate collision risk for a ship operating in a two-lane canal. According to rule 9 of the Colreg-72 navigation rules, in a narrow canal, a vessel shall keep as near to the wall that lies on its starboard side. However, a busy harbor entered through a narrow canal still presents impact hazards. Certain conditions in a two-lane canal, such as a head-on situation in the straight part of the canal during an overtaking maneuver and large curvature of a turning maneuver in the bend part of the canal, could lead to accidents. In the first condition, the ship alters its own course to the port side to overtake another ship in the same lane but the course altered is too large and hits the wall of the canal. In the second condition, the target ship may take an excessively large turn on the bend part of the canal, causing collision with the ship on the opposite lane. Collision risk is represented as the risk of damage to the ship structure and includes the probability of impact accident and severity of structural damage. Predictions of collision probabilities in a two-lane canal have been developed based on a simulation of ship maneuvering using a mathematical maneuvering group (MMG) model and automatic identification system (AIS) data. First, the propeller revolution and rudder angle of the subject ship are simulated to determine safe trajectories in both parts of the canal. Second, impact accidents are simulated for both conditions. The ship’s speed, and current and wind velocity are randomly simulated based on the distribution of the AIS and environment data for the research area. The structural consequences of the impact accident are measured as collision energy losses, based on the external dynamics of ship collision. The research area of the two-lane canal is located at the Madura Strait between the Java and Madura islands in East Java of Indonesia, as shown by the red line in Figure 1. A project for developing a new port and dredging a new two-lane canal to facilitate an increase in the number of ship calls is currently underway in the research area. Figure 1 shows the ships’ trajectories plotted using the AIS data as on January 1, 2011. The trajectories are mostly seen to be coming out of the canal, confirming that it is shallow and needs to be dredged.
Roads are land transportation infrastructure that covers all parts of the road. Roads with bad conditions will interfere with the achievement of activities to a destination. The situation also includes damage to the road surface in the form of holes. To overcome this, in this Final Project a hole detector was detected in the road using the Gray Level Co-occurrence Matrix (GLCM) and Neural Network (NN). The tool detects holes in the surface of the road using a camera by walking along the road being examined. The camera is used instead of the eye to detect road surface damage. The method used to detect holes is the GLCM. The GLCM method produces several features, namely entropy, contrast, energy, homogeneity, and correlation which will then be processed using a NN to produce a decision whether there is a hole or not. In addition to knowing where the location of the damage is equipped with GPS (Global Positioning System). The results of image feature extraction using the GLCM and road classification using NN can be used in the hole detection process. Testing is done using a car prototype that is monitored through the computer. The percentage of successful hole detection is 86.6% using 10 hidden. When a hole is detected the device manages to take a picture, then sends the hole coordinates to the server.
This paper provides a method to analyze the safety factor of the hawser line between Floating Storage Offloading (FSO) and Single Point Mooring (SPM) by using motions simulation. The simulation was conducted based on irregular wave arrival for 1 year and 100 years. A case study for a mooring system of a 55,081 tons displacement of FSO tanker to a 360 tons displacement of SPM buoy was simulated with the existing hawser line length of 45 m and the new hawser line length of 70 m. The wave forces and moments are computed using Ansys Aqwa based on the 3D Panel Method both in the frequency domain and time domain. The wave periods are 6 s and 8 s for the 1-year and 100-year data, respectively. The safety factor of the hawser line significantly improves by using the new hawser line.
Seakeeping performance is one of the requirements to ensure the convenience and safety of life at sea, especially for passenger ships. In this paper, the result of computational fluid dynamics (CFD) analyses concerning the effect of NACA 4412 stern foil to the improvement of seakeeping quality and resistance of 1200 GT passenger ship are presented. The fitting of stern foil has become a popular method for the improvement of ship design. Stern foil is hydrofoil attached to the transom of the vessel located at the stern part below the waterline. The analysis was developed on the conditions of regular and irregular head waves. The CFD computation on 8 variations of the stern foil position and attack angle has been carried out by using Numeca Fine/Marine. The study confirms that the ship resistance decrease by 3.6 % at the service speed of the ship. The improvement of heaving and pitching amplitudes, as well as heaving acceleration, are significant for the hull vane located at 50% of displacement draft and 1% of displacement length with the attack angle of −4°.
This paper proposes an estimation method for ships on collision courses taking crash astern maneuvers based on a new potential area of water (PAW) for maneuvering. A crash astern maneuver is an emergency option a ship can take when exposed to the risk of a collision with other ships that have lost control. However, lateral forces and yaw moments exerted by the reversing propeller, as well as the uncertainty of the initial speed and initial yaw rate, will move the ship out of the intended stopping position landing it in a dangerous area. A new PAW for crash astern maneuvers is thus introduced. The PAW is developed based on a probability density function of the initial yaw rate. Distributions of the yaw rates and speeds are analyzed from automatic identification system (AIS) data in Madura Strait, and estimated paths of the maneuvers are simulated using a mathematical maneuvering group model.
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