The Performance of a Spectral Wave Model at Predicting Wave Farm Impacts
- Medium-sized arrays of 27 WECs are modeled, representing a medium-sized wave farm. Smaller arrays of 5 WECs were compared in McNatt et al. , and, while larger arrays could be considered in the future, an array of 27 WECs represents a medium-sized wave farm and is a stepping stone in both the development of commercial wave farms and the verification of SNL-SWAN. To model these arrays, an approach referred to as Linear Wave Interaction Theory (LWIT) was used.
- Only directionally spread sea conditions are modeled, based on literature on the range of realistic directional spreading . The range of directional spreading includes highly focused storm waves, however it does not include idealized purely unidirectional seas. In previous studies, SNL-SWAN did not show good agreement with linear wave theory for unidirectional seas because without the SWAN diffraction option on, it is not capably of wave diffraction . However, directional spreading is a form of wave diffraction and unidirectional seas are not representative of real sea conditions.
- A realistic beach bathymetry is used. In previous studies, the authors considered only a flat sea bottom. However, the aim of SNL-SWAN is to assess the impact of wave farms on nearshore processes. As such, the wave fields produced by each method were fed in as boundary conditions for a SWAN model that propagates the wave field to shore.
2.1. Computational Methods
2.1.2. Linear Wave Interaction Theory
2.2. WEC Arrays
2.4. Computational Domains
- Array Domain: the domain around the WEC array located offshore. This domain is evaluated either with SNL-SWAN or with LWIT. The bathymetry is a flat bottom.
- Nearshore Domain: the domain with a beach-profile bottom extending to a depth of 0 at the shoreline. It is modeled always with SWAN.
2.5. Means of Comparison
2.5.2. Normalized Error
2.5.3. Mean-Squared Skill Score
- would be produced by not modeling the WEC array at all.
- : SNL-SWAN produces worse results compared to not modeling the WEC array at all.
- : SNL-SWAN exactly matches LWIT results (no error).
- : SNL-SWAN makes some improvement to the wave field (compared to not modeling the array at all).
3.1. Example Results
3.2. General Considerations
3.2.1. Wave Breaking
3.2.2. Boundary Condition Effects
3.2.3. Garden Sprinkler Effect
3.3. Mean-Squared Skill Score Results
3.4. Visualization of Results
- How significant are the impacts of the array?
- Are the errors between SNL-SWAN and LWIT meaningful in the context of nearshore physical processes?
Conflicts of Interest
|Significant wave height|
|LWIT||Linear wave interaction theory|
|MSSS||Mean-squared skill score|
|RCW||Relative capture width|
|SNL||Sandia National Laboratories|
|SWAN||Simulating WAves Nearshore|
|Peak wave period|
|WEC||Wave energy converter|
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McNatt, J.C.; Porter, A.; Chartrand, C.; Roberts, J. The Performance of a Spectral Wave Model at Predicting Wave Farm Impacts. Energies 2020, 13, 5728. https://doi.org/10.3390/en13215728
McNatt JC, Porter A, Chartrand C, Roberts J. The Performance of a Spectral Wave Model at Predicting Wave Farm Impacts. Energies. 2020; 13(21):5728. https://doi.org/10.3390/en13215728Chicago/Turabian Style
McNatt, J. Cameron, Aaron Porter, Christopher Chartrand, and Jesse Roberts. 2020. "The Performance of a Spectral Wave Model at Predicting Wave Farm Impacts" Energies 13, no. 21: 5728. https://doi.org/10.3390/en13215728