Accurate prediction of long-term extreme loads is essential for the design of wave energy converters (WECs), but it is also computationally demanding due to the low probabilities associated with their occurrence. Although a full long-term probabilistic analysis using integration over all sea states or Monte Carlo simulation (MCS) may be used, these methods can be prohibitively expensive when individual response simulations are complex and time-consuming. The application of polynomial chaos expansion (PCE) schemes to allow the propagation of uncertainty from the environment through the stochastic sea surface elevation process and ultimately to WEC extreme load response prediction is the focus in this study. A novel approach that recognizes the role of long-term ocean climate uncertainty (in sea state variables such as significant wave height and spectral peak period) as well as short-term response uncertainty arising from the unique random phasing in irregular wave trains is presented and applied to a single-body point-absorber WEC device model. Stochastic simulation results in time series realizations of various response processes for the case-study WEC. We employ environmental data from a possible deployment site in Northern California (NDBC 46022) to assess long-term loads. MCS computations are also performed and represent the “truth” system against which the efficiency and accuracy of the PCE surrogate model are assessed. Results suggest that the PCE approach requires significantly less effort to obtain comparable estimates to MCS.

A wide range of methods have been proposed for the derivation of environmental contours for marine structures that must meet reliability targets. An environmental contour is a set of joint extremes of environmental conditions associated with a target return period. In general, environmental contour methods help with the prediction of some future critical combinations of environmental conditions (e.g., wind, waves, current) at a location of interest based on a limited dataset, thus allowing designers to ensure a prescribed structural reliability. In fact, some of these contour methods are specifically recommended by technical specifications and standards as part of a design process. This paper outlines the rules and procedures for a collaborative benchmarking exercise — focused on open comparison — in which researchers are invited to develop and present their own contour derivation approaches based on common datasets that will be available to all. Hindcast and observational datasets are considered and two exercises are planned: One focuses on applying environmental contour methods to a wide range of datasets and the other focuses on uncertainty characterization. Besides describing the benchmark’s methodology, this paper presents baseline results of computed contours following current recommendations. The overall goals of this endeavor are: (i) to work towards the development of more robust statistical models and contour construction methods, (ii) to encourage increased discussion in the international research community and among practitioners, and (iii) to support ongoing efforts to improve technical specifications and standards.