Incidence of Rank Correlation Algorithm on the Assessment of Bimodal Sea State Conditions Based on Ship Motion Analysis

The reliable assessment of sea state conditions, experienced by a ship advancing in a seaway, is a key factor to avoid potential dynamic instabilities at quartering and following seas, reduce possible injuries of the crew, due to sliding or tipping events, increase the wellness of passengers by reducing the motion sickness incidence and avoid possible cargo loss. In this respect, ships receive a variety of forecast data by wave buoys and weather satellites, such as those ones provided by the European Center for Medium-Range Weather Forecasts. Nevertheless, most of measured wave data are acquired along the coastlines and provided on a large scale, making almost impossible to obtain a reliable assessment of local sea state conditions experienced by the ship along its route. In this respect, thanks to the employment of the wave buoy analogy, it is possible to overcome this lack and obtain unvaluable information from the measurement and spectral analysis of ship motions and accelerations. Indeed, most of past research activities focused on the assessment of wave spectra for unimodal sea state conditions, even if the seaway experienced by the ship generally consists of both wind-sea and swell components, with different heading angles, significant wave heights and wave peak periods. Hence, the aim of the paper is the development and preliminary testing of a new algorithm, based on the employment of spectral correlation coefficients for the assessment of bimodal sea state conditions, starting from the measurement and analysis of ship accelerations. Pearson and Spearman rank correlation coefficients are evaluated and compared to detect the best correlation algorithm in different sea state conditions. The analysis is conducted considering the S-175 containership as reference vessel. Ship motions are simulated by a purposely developed code in the time-domain.