In this article control of ship on a transoceanic route is represented as multicriteria optimization problem. Optimal route can be found by minimizing the objective function expressed as ship integral work for a voyage, taking into account ship’s schedule, weather conditions, engine loads and risks connected with ship dynamics in waves. The risk level is represented as non-linear function with heterogeneous input variables which estimated by means of multi-input fuzzy inference system on the basis of pre-calculated or measured ship motion parameters. As the result of this research the optimal transoceanic route planning algorithm is obtained.
The research addresses the problem of an ultra-large container ship mathematical model adjustment based on sea trials. In order to verify the model’s adequacy, simulated data had to be compared to the trial report data, which was obtained in ballast condition with significant trim. In such circumstances, model coefficients cannot be calculated by known methods and have to be corrected as per trial data. It is proposed to determine translational motion coefficients first. To get optimal results, it was also proposed to divide the objective function into kinematic and dynamic components, with each component being assigned a weighting factor. A separate objective function component was assigned to the zig-zag maneuver, which includes the first and second overshoot angles.
This study addresses the problem of training the officers, which are assigned to an electrical-driven vessels equipped with azimuth thrusters. A pair of omnidirectional thrusters in combination with power plant system containing several diesel generators imply a potential for a variety of different emergency scenarios, which also includes partial or full loss of control or blackout. These fault scenarios were classified in the article with predefined risk levels depending on the area, time limitation, mode of operation and fault itself. Mutual responsibilities and action algorithms for bridge and engine teams in a step-by-step manner have been developed for each scenario. Personnel behavioral differences in both expected and unexpected emergencies have also been studied.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.