hrvatski

Machine learning techniques for modeling ships performance in waves

This paper presents a design of a system for monitoring and recording the influence of a running sea on a vessel in motion. Our approach is based on machine learning techniques that relate measured wave parameters (encounter angle, wave height and wave amplitude) with measured motion characteristics of the vessel. High quality GRIB data for wave measurements are available for some regions (e.g. North Sea and Adriatic) and we use those for generating training sets. We store this correlation in a neural net and use this information in conjunction with the targeted performance indicator (RMS of linear acceleration, RMS of roll or pitch angle, fuel consumption) to create historical directed performance charts for the vessel in consideration. We use this information for rational route planning and optimization. We report on the conclusions of experiments.

polar diagram ; IMU sensor ; machine learning ; performance optimization

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