AbsfracC This paper describes the steps for building the time domain model of an ocean waves energy converter device called AWS. This model will be useful for the design of such devices and for the prediction of its behavior in different sea conditions. Besides the inherent complexity associated to the nonlinear dynamics of the AWS components, for time domain simulation, a convolution integral is conventionally used to model the hydrodynamic diffraction and radiation forces, being the last one characterized in the frequency domain by the added mass and damping. However, this integral is relatively expensive to evaluate and difficult to use with efficient integration routines.Here an efficient methodology mixing frequency and timedomain is introduced to deal with this problem. The wave transfer functions were first obtained from a numerical finite element code, as vectors containing the frequency response of the desired transfer functions. The data obtained is then used to identify the corresponding analytic form of the transfer functions, which is integrated in the time-domain model. The model includes the slow and fast dynamics of the AWS behavior.

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Abstract. Using the time-series of significant wave height and the peak period between 1979 and 2009 generated by SOWFIA project, some relevant statistical information about energy content available in ocean waves in Cape-Verde is obtained. The monthly and annual time-series of the average power are analysed and the confidence intervals for their values are defined. Considering all of the 31 years of data, the results show that the most energetic month, from the average power point of view is January (23.49 kW/m) and the least energetic month is July (15.04 kW/m). In fact, the monthly average power decays from January to July and increases from July to December (21.21 kW/m). The annual average power exhibits a clear attenuation over the 31 years analysed, the reason for which is not yet clear to us. However, using the appropriate Autoregressive Integrated Moving Average (ARIMA) model it is possible to estimate that future values of the annual average power tend to oscillate around 18.2 kW/m. Through the Coefficient of Variation of Power (COVP), obtained by dividing the standard deviation of the power time-series by the average power, it is possible to conclude that the wave resource is stable, with COVP between 0.46 and 0.66. The values of the Monthly Variation Index (MVI), the maximum range of the monthly mean wave power relative to the yearly mean level, show that the resource is relatively stable, with MVI < 1.2. The present work calculates the available power input into the Natural Caves (NCs) in Cape Verde Islands, through a rigorous analysis of the wave climate that excites them. The minimum sampling size and the corresponding numbers of days of measurements per month, are also estimated.