Search

The autonomous operations of marine vehicles have recently attracted significant attention. In particular, automation of harbor operations, such as autonomous docking or berthing, is a challenging target. In low-speed harbor operations, the external disturbance of wind is a non-negligible factor. Therefore, the stochastic behavior of gusty wind should be considered in numerical simulations. This study presents a practical computational scheme for generating random wind velocity fields. In the proposed method, the von Kármán's spectrum [1] and Hino's wind speed spectrum [2] were fitted by a degree of freedom (1DoF) filter driven by the Wiener process. The corresponding Itô's equation had the form of the Ornstein–Uhlenbeck process. By employing the analytical solution of the Ornstein–Uhlenbeck process, the wind process was successfully generated without time series repetition. Furthermore, the wind speed and direction processes were also generated from the measured drift and diffusion terms. The spectra of these numerical results are consistent with the observed spectra. [ABSTRACT FROM AUTHOR]

So far, we have developed a calculation method for roll angular acceleration and its derivative, which is a jerk. In this study, using the probability density function (PDF) line integral method and the joint PDF (JPDF) of roll angle and angular rate, we obtain a theoretical method for the evaluation of the two properties. In particular, the estimation method of acceleration is considered crucial, as the angular acceleration is directly related to inertial forces or moments of onboard objects. Moreover, to consider the strength of container stacks, as well as the lashing strength of cargo and track on board, a separate evaluation of roll angle and roll angular acceleration is insufficient. Further, although the new-generation intact stability criteria for excessive acceleration have been discussed by the International Maritime Organization, the evaluation of JPDF of roll angle and roll angular acceleration could contribute to the efficient evaluation of the safety of crews on board. Meanwhile, no JPDF of roll angle or angular acceleration has been calculated for an arbitrary nonlinear system so far, and there is no doubt that it is a challenging problem. In this study, by applying and extending the idea of the PDF line integral method, we proposed a method for evaluating the JPDF of roll angle and angular acceleration. [ABSTRACT FROM AUTHOR]

Recently, a considerable number of research and development projects have focused on automatic vessels. A highly realistic simulator is needed to validate control algorithms for autonomous vessels. For instance, when considering the automatic berthing/unberthing of a vessel, the effect of wind in such low-speed operations cannot be ignored because of the low rudder performance during slow harbor maneuvers. Therefore, a simulator used to validate an automatic berthing/unberthing control algorithm should be able to reproduce the time histories of wind speed and wind direction realistically. Therefore, in our first report on this topic, to obtain the wind speed distribution, we proposed a simple algorithm to generate the time series and distribution of wind speed only from the mean wind speed. However, for wind direction, the spectral distribution could not be determined based on our literature surveys, and hence, a simple method for estimating the coefficients of the stochastic differential equation (SDE) could not be proposed. In this study, we propose a new methodology for generating the time history of wind direction based on the results of Kuwajima et al.’s work. They proposed a regression equation of the standard deviation of wind direction variation for the mean wind speed. In this study, we assumed that the wind direction distribution can be represented by a linear filter as in our previous paper, and its coefficients are derived from Kuwajima’s proposed equation. Then, as in the previous report, the time series of wind speed and wind direction can be calculated easily by analytically solving the one-dimensional SDE. The joint probability density functions of wind speed and wind direction obtained by computing them independently agree well with the measurement results. [ABSTRACT FROM AUTHOR]