Abstract
Wave tank testing is a useful tool to assess the
performance of Wave Energy Converters (WEC) at different
technology readiness levels (TRL). At early TRL the use of
systematically varying wave conditions is acceptable,
however at later stages there is a need to use testing
conditions representative of potential prototype
deployment sites. Environmental data at these deployment
sties can be collected by various instruments, such as buoys,
ADCP, radars etc. The most commonly used method to recreate the measured environmental testing conditions in a
wave tank is to represent a sea state using the measured
significant wave height and peak period and a parametric
wave spectrum such as the JONSWAP or PiersonMoskowitz spectrum. Although a useful tool, these
parametric spectra represent a simplification which omits
much of the site specific spectral information, such as the
directional information of the sea state, which has the
potential to significantly impact WEC performance. In most
wave tanks it is possible to reproduce directly a measured
spectrum. However this raises questions about which
measured cases to reproduce. The use of HF radar data
provides the opportunity to select sea states for wave tank
testing that are more representative of a potential
deployment site. It is necessary to develop a methodology to
systematically select a set of representative experimental
test cases from the wave datasets obtained from HF radar.
Previous research has demonstrated the use of K-means
clustering to obtain representative wave cases from
measured wave spectrum. Here the expansion of this
method is demonstrated. HF radar data obtained at Wave
Hub, a wave energy test site in Cornwall, UK, and buoy data
obtained close to Long Island, USA are used in this study.
Original language | English |
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Number of pages | 0 |
Journal | https://proceedings.ewtec.org/? |
Volume | 0 |
Issue number | 0 |
Early online date | 3 Sept 2019 |
Publication status | Published - 3 Sept 2019 |
Event | 13th European Wave and Tidal Energy Conference - Duration: 1 Sept 2019 → 6 Sept 2019 |