The paper is focused on the formulation of an adequate criterion for associating wave storm events to the generating wind storm ones, and on the study of correlation between their characteristic parameters. In this context, the sea storm definition commonly used for storm identification from significant wave height data is adapted for wind storm, by processing wind speed data. A sensitivity analysis is proposed as function of the storm thresholds aiming at identifying optimal combination of wind speed and significant wave height thresholds allowing the association of relatively large number of events ensuring high correlation between wind and wave storm parameters. The analysis is carried out using as input data wind speeds and significant wave heights from four meteorological (buoys and anemometers) stations of the National Data Buoy Center moored off the East Coast of the United States. Results reveal that an optimal threshold combination is achieved assuming both wind speed and significant wave height threshold as 1.5 time their respective averages.

The design of any wave energy converter involves the determination of relevant statistical data on the wave energy resource oriented to the evaluation of the structural reliability and energy performance of the device. Currently, limited discussions concern the estimation of parameters connected to the energy performance of a device. Thus, this paper proposes a methodology for determining average downtime and average missed energy, which is the energy that is not harvested because of device deactivations during severe sea storms. These quantities are fundamental for evaluating the expected inactivity of a device during a year or during its lifetime and are relevant for assessing the effectiveness of a device working at a certain site. For this purpose, the equivalent power storm method is used for their derivation, starting from concepts pertaining to long-term statistical analysis. The paper shows that the proposed solutions provide reliable estimations via comparison with results obtained by processing long wave data.

A small-scale field experiment was conducted on a U-OWC incorporated into a caisson breakwater at the NOEL laboratory of Reggio Calabria (Italy). The U-Oscillating Water Column (U-OWC) or REWEC (REsonant Wave Energy Converter) is a device belonging to the family of OWCs. Such a device is very innovative, being able to absorb a very high percentage of incoming sea waves energy and to produce electrical power via proper PTO. The focus of the paper has been the analysis of the impact wave loads acting on the modified U-OWC structure during extreme wave events. A total of 250 records of pure wind waves were analyzed to verify the behaviors of wave loads acting on a U-OWC breakwater during operating conditions. The occurrence of both “quasi-standing wave” loads due to non-breaking waves and “impulsive wave loads”, exerted by a wave breaking against the U-OWC model, were observed. Then, Goda’s model was applied to predict the wave pressure distribution on the external wall of the U-OWC pneumatic chamber, and the theoretical results were compared to those obtained via small-scale field experiment.

Measuring the free-surface displacement on a vertical wall of a marine structure is not a trivial problem. In this context, the efficacy of ultrasonic probes is affected by the interaction between the signal emitted by the sensor and the vertical wall, whereas image-based techniques are computationally demanding, especially if long-time series are utilized. Considering these difficulties, this paper proposes a novel approach for measuring the sea surface elevation on vertical breakwaters. The proposed methodology involves the use of pressure measurements and a reconstruction algorithm based on a compressive sensing (CS) technique in conjunction with a generalized harmonic wavelet (GHW) basis. In particular, a constrained CS optimization approach is proposed by utilizing the known values of the free-surface data to reconstruct all other missing data while adhering at the same time to prescribed upper and lower bounds at all time instants. The reliability of the methodology was assessed against field data pertaining to a vertical wall equipped with pressure transducers recorded at the Natural Ocean Engineering Laboratory of Reggio Calabria. It was shown that direct application of an unconstrained GHW-based CS optimization approach yielded physically inconsistent minima and maxima values; thus, it was inadequate for reliably reconstructing the free surface. These drawbacks were removed by the constrained GHW-based CS. Furthermore, examination of the reconstructed sea surface profiles in the vicinity of extremely high wave crests or wave troughs showed that they are in agreement with pertinent theoretical data obtained by using the nonlinear quasi-determinism theory.

This article deals with the case study of a marina located in Roccella Jonica (Italy), where a wave energy harvester belonging to the family of U-oscillating water columns (U-OWC) is going to be installed. U-OWCs are wave energy harvesters composed of a water column exposed to the action of random sea waves and an air pocket connected to the atmosphere by a power take—off (PTO) system. In Roccella Jonica, this device is going to be embedded in a vertical breakwater expanding the main layout of the infrastructure. For ensuring the structural safety of the system, to characterize statistically its response peaks in severe environmental conditions is important. In this context, one of the main difficulties is utilizing appropriate environmental conditions representing real extreme events at the installation site. This article proposes to adopt the DNV trapezoidal storm model for representing the time history of an extreme event in conjunction with a nonlinear U-OWC model. Relevant Monte Carlo simulations show that the DNV storm model provides peak distributions that are rather close to the ones obtained by processing real storm time histories. Thus, it can be adopted for checking the performance of the system in extreme conditions.