Characterization of U.S. Wave Energy Converter (WEC) Test Sites: A Catalogue of Met-Ocean Data.

Dallman, Ann; Neary, Vincent S.

doi:10.2172/1160290

Cited by 21 publications

(14 citation statements)

References 11 publications

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“…AND NATIONAL DATA BUOY CENTER BUOY (ADOPTED FROM [10]). heave motion specified in Configuration A. Heave and pitch degrees of freedom were considered because these displacements are typically limited with an end stop in power take-off (PTO) systems.…”

Section: Figure 2 One-hundred-year Contour From the Coastal Data Infmentioning

confidence: 99%

“…For simplicity, the behavior of the PTO system was not modeled. Figure 2 shows the sea state measurements and the estimated 100-year contour [10]. We then selected five representative wave environments along the 100-year contour to perform our short-term response analysis using the MLER method, listed in Tab.…”

Section: Figure 2 One-hundred-year Contour From the Coastal Data Infmentioning

confidence: 99%

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Application of the Most Likely Extreme Response Method for Wave Energy Converters

Quon

Platt

et al. 2016

Volume 6: Ocean Space Utilization; Ocean Renewable Energy

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Extreme loads are often a key cost driver for wave energy converters (WECs). As an alternative to exhaustive Monte Carlo or long-term simulations, the most likely extreme response (MLER) method allows mid- and high-fidelity simulations to be used more efficiently in evaluating WEC response to events at the edges of the design envelope, and is therefore applicable to system design analysis. The study discussed in this paper applies the MLER method to investigate the maximum heave, pitch, and surge force of a point absorber WEC. Most likely extreme waves were obtained from a set of wave statistics data based on spectral analysis and the response amplitude operators (RAOs) of the floating body; the RAOs were computed from a simple radiation-and-diffraction-theory-based numerical model. A weakly nonlinear numerical method and a computational fluid dynamics (CFD) method were then applied to compute the short-term response to the MLER wave. Effects of nonlinear wave and floating body interaction on the WEC under the anticipated 100-year waves were examined by comparing the results from the linearly superimposed RAOs, the weakly nonlinear model, and CFD simulations. Overall, the MLER method was successfully applied. In particular, when coupled to a high-fidelity CFD analysis, the nonlinear fluid dynamics can be readily captured.

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Section: Figure 2 One-hundred-year Contour From the Coastal Data Infmentioning

confidence: 99%

Section: Figure 2 One-hundred-year Contour From the Coastal Data Infmentioning

confidence: 99%

Application of the Most Likely Extreme Response Method for Wave Energy Converters

Quon

Platt

et al. 2016

Volume 6: Ocean Space Utilization; Ocean Renewable Energy

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show abstract

“…The selected wave cases have periods of T = 4, 6, 8, 12 s, which represent short-(25 and 56 m), medium-(100 m), and long-wavelength (212 m) waves, respectively. The medium and long wave periods were chosen because they are representative of sea states at potential WEC deployment sites in the U.S. [21]. The short wave cases were chosen due to their high potential for wave scattering.…”

Section: Wave Conditionsmentioning

confidence: 99%

Comparison of Numerical Methods for Modeling the Wave Field Effects Generated by Individual Wave Energy Converters and Multiple Converter Wave Farms

McNatt¹,

Porter²,

Ruehl

2020

JMSE

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This numerical study compares the wave field generated by the spectral wave action balance code, SNL-SWAN, to the linear-wave boundary-element method (BEM) code, WAMIT. The objective of this study is to assess the performance of SNL-SWAN for modeling wave field effects produced by individual wave energy converters (WECs) and wave farms comprising multiple WECs by comparing results from SNL-SWAN with those produced by the BEM code WAMIT. BEM codes better model the physics of wave-body interactions and thus simulate a more accurate near-field wave field than spectral codes. In SNL-SWAN, the wave field’s energy extraction is modeled parametrically based on the WEC’s power curve. The comparison between SNL-SWAN and WAMIT is made over a range of incident wave conditions, including short-, medium-, and long-wavelength waves with various amounts of directional spreading, and for three WEC archetypes: a point absorber (PA), a pitching flap (PF) terminator, and a hinged raft (HR) attenuator. Individual WECs and wave farms of five WECs in various configuration were studied with qualitative comparisons made of wave height and spectra at specific locations, and quantitative comparisons of the wave fields over circular arcs around the WECs as a function of radial distance. Results from this numerical study demonstrate that in the near-field, the difference between SNL-SWAN and WAMIT is relatively large (between 20% and 50%), but in the far-field from the array the differences are minimal (between 1% and 5%). The resultant wave field generated by the two different numerical approaches is highly dependent on parameters such as: directional wave spreading, wave reflection or scattering, and the WEC’s power curve.

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“…Examples of this application can be found in Dallman and Neary (2014) and Vanem and Bitner-Gregersen (2014). In some cases, environmental contours for a 100-year return period calculated using this traditional application fail to cover the observations taken over a relatively short (8-to 10-year) period of record.…”

Section: Wave Energy Converter Survivability Analysismentioning

confidence: 99%

“…In some cases, environmental contours for a 100-year return period calculated using this traditional application fail to cover the observations taken over a relatively short (8-to 10-year) period of record. This problem was addressed in Dallman and Neary (2014) and by inflating the contour resulting from the I-FORM using an arbitrary percentage value. The shortcomings of the environmental contours generated using the traditional methodology along with attempts to correct these problems as a post-processing step clearly show that this methodology requires further exploration and modification in order to generate more realistic contours.…”

Section: Wave Energy Converter Survivability Analysismentioning

confidence: 99%

Modified Inverse First Order Reliability Method (I-FORM) for Predicting Extreme Sea States.

Eckert-Gallup¹

2014

Self Cite

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I would like to thank my advisor, Mark Stone, and my committee members Julie Coonrod and Vincent Neary for their guidance and support over the course of my degree and research. I would also like to thank my mentor at Sandia National Laboratories, Cédric Sallaberry, for everything that he has taught me and for all of the encouragement that he has given me. The body of this work has been submitted as a journal article with coauthors Cédric Sallaberry, Ann Dallman, and Vincent Neary. I thank each of these individuals for their collaboration and thank Ann and Vince for allowing me to be a part of their project.

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Characterization of U.S. Wave Energy Converter (WEC) Test Sites: A Catalogue of Met-Ocean Data.

Cited by 21 publications

References 11 publications

Application of the Most Likely Extreme Response Method for Wave Energy Converters

Application of the Most Likely Extreme Response Method for Wave Energy Converters

Comparison of Numerical Methods for Modeling the Wave Field Effects Generated by Individual Wave Energy Converters and Multiple Converter Wave Farms

Modified Inverse First Order Reliability Method (I-FORM) for Predicting Extreme Sea States.

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