Long-term extreme response analysis for semi-submersible platform mooring systems

Abstract

The accurate assessment of long-term extreme responses of floating-structure mooring system designs is important because of small failure probabilities caused by long-term and complex ocean conditions. The most accurate assessment would involve considering all conceivable sea states in which each sea state is regarded as a stochastic process and performing nonlinear time-domain numerical simulations of mooring systems to estimate the extreme response from a long-term analysis. This procedure would be computationally intensive because of the numerous short-term sea states involved. Here, a more feasible approach to evaluate the long-term extreme response is presented through immediate integration combined with Monte Carlo simulations. A parameter fitting procedure of the short-term extreme response distribution under irregular wave conditions is employed to solve the long-term response integration. Case studies were conducted on a semi-submersible platform using environmental data measurements of the Gulf of Mexico and a joint distribution model of the environmental parameters was considered. This approach was observed to be effective and the results were compared with those of traditional methodologies (univariate extreme value design and environmental contour methods). The differences were reflected using a reliability analysis of mooring lines, which indicated that the design standards must be stricter when using long-term analysis.

Investigations of the long-term extreme buffeting response of long-span bridges using importance sampling Monte Carlo simulations

“…However, these methods converge to approximate solutions and struggle when the limit state function is highly nonlinear [33,34]. On the other hand, estimations of the long-term extreme response obtained through Monte Carlo simulations are recognized as being more robust than reliability methods [35][36][37][38]. The computational efficiency of Monte Carlo simulations can be improved by importance sampling Monte Carlo (ISMC) simulations, which simulate samples in the region that contributes most to the response evaluation [39].…”