In July 2016, the international wave power company Eco Wave Power commissioned MetOcean Solutions to provide wave statistics for a nearshore site in Cuyutlan, Manzanillo, on the west coast of Mexico.
“Our company required wave statistics to assess whether it was feasible to install a wave power plant at the location”, says Eco Wave Power project manager Guillermo Sherwell. “An overview of the wave conditions is essential for the planning of offshore installations - it allows us to assess operability and identify potential hazards. The data also helps document important environmental conditions that may require further attention.”
Dr Séverin Thiébaut from MetOcean Solutions was in charge of the project, while Dr Rafael Guedes led the wave model implementations.
“We set up a multi-nest wave hindcast model to replicate the wave climate at the nearshore site,” explains Séverin. “We modelled the 2005-2014 period so that we could reproduce the ambient wave climate and reliably estimate the most extreme wave conditions that can occur in a 30-year period."
In consultation with the client, the team picked three nearshore sites representative of the proposed location of the wave power plant in water depths of 4, 6 and 8 m. Annual, seasonal and monthly wave analyses were carried out for these sites to extract ambient and extreme wave statistics to assess the design, workability and efficiency of the proposed wave power plant. .
The Simulating WAves Nearshore (SWAN) model was used for the work. A 4-level nesting approach was applied to downscale wave spectra from a global model to the shallow nearshore locations of interest. Wind fields for the model were derived from the Climate Forecast System Reanalysis (CFSR), and tidal constituents from the Oregon State University Tidal Inverse Solution (OTIS). The site is prone to tropical cyclones, and a cyclone mask was used to remove tropical cyclone signatures from the hindcast metocean data.
The model outputs were used to calculate wave power, the rate at which energy is being transmitted. Fatigue analysis was assessed by estimating the total number of individual waves of varying height and period.
“We produced regional summary maps of the conditions,” explains Séverin. “These showed the spatial distribution of variables such as mean significant wave height for the total, swell and sea components, mean peak wave period, mean wave direction and wavelength.” (Illustrated in Figure 1). Joint probability occurrences of variables such as significant wave height and peak period were also included (as illustrated in Figure 2).
Wind statistics were also generated, including monthly and annual wind speed exceedance probabilities, joint probabilities of wind speed and direction and corresponding wind roses (illustrated in Figure 3).
“Values such as the 99th percentile non-exceedance significant wave height (Hs) level is often used to assess the wave climate and structure design for energetic events. This denotes the significant wave height which is not exceeded for 99% of the time.” states Séverin. “Similarly useful are extreme metocean statistics like the return period values for wind and wave, i.e. the likely maximum that can occur within a specific extended duration.”
“We are very happy with the quality of the work,” states Guillermo Sherwell from Eco Wave Power.