Extreme value statistics of non-Gaussian random wave fields and the airgap problem for offshore platforms

摘要

The paper describes a new method for estimating the extreme values of a random field in both space and time. The method relies on the use of data provided by measurements or Monte Carlo simulations combined with a technique for estimating the extreme value distribution of a recorded time series. The time series in question represents the spatial extremes of the random field at each point in time. The time series is constructed by sampling the available realization of the random field over a suitable grid defining the domain in question and extracting the extreme value. This is done for each time point of a suitable time grid. Thus, the time series of spatial extremes is produced. This time series provides the basis for estimating the extreme value distribution using available techniques for ordinary time series, which the authors have already developed, and which results in an accurate practical procedure for solving a very difficult problem. Several examples of applications to non-Gaussian random fields associated with the airgap problem for offshore platforms are given. It is shown that it is very important to account for area effects to obtain good predictions.