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In recent years, the global transition towards green energy, driven by environmental concerns and increasing electricity demands, has remarkably reshaped the energy landscape. The transformative potential of marine wind energy is particularly critical in securing a sustainable energy future. To achieve this objective, it is essential to have an accurate understanding of wind dynamics and their interactions with ocean waves for the proper design and operation of offshore wind turbines (OWTs). The accuracy of met-ocean models depends critically on their ability to correctly capture sea-surface drag over the multiscale ocean surface—a quantity typically not directly resolved in numerical models and challenging to acquire using either field or laboratory measurements. Although skin friction drag contributes considerably to the total wind stress, especially at moderate wind speeds, it is notoriously challenging to predict using physics-based approaches. The current work introduces a novel approach based on a convolutional neural network (CNN) model to predict the spatial distributions of skin friction drag over wind-generated surface waves using wave profiles, local wave slopes, local wave phases, and the scaled wind speed. The CNN model is trained using a set of high-resolution laboratory measurements of air-side velocity fields and their respective surface viscous stresses obtained over a range of wind-wave conditions. The results demonstrate the capability of our model to accurately estimate both the instantaneous and area-aggregate viscous stresses for unseen wind-wave regimes. The proposed CNN-based wall-layer model offers a viable pathway for estimating the local and averaged skin friction drag in met-ocean simulations.
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Improving Wave‐Based Air‐Sea Momentum Flux Parameterization in Mixed Seas
In winter, the Northwest Tropical Atlantic Ocean can be characterized by various wave age-based interactions among ocean current, surface wind and surface waves, which are critical for accurately describing surface wind stress. In this work, coupled wave-ocean-atmosphere model simulations are conducted using two different wave roughness parameterizations within COARE3.5, including one that relies solely on wind speed and another that uses wave age and wave slope as inputs. Comparisons with the directly measured momentum fluxes during the ATOMIC/EUREC4A experiments in winter 2020 show that, for sea states dominated by short wind waves under moderate to strong winds, the wave-based formulation (WBF) increases the surface roughness length in average by 25% compared to the wind-speed-based approach. For sea states dominated by remotely generated swells under moderate to strong wind intensity, the WBF predicts significantly lower roughness length and surface stress (≈15%), resulting in increased near-surface wind speed above the constant flux layer (≈5%). Further investigation of the mixed sea states in the model and data indicates that the impact of swell on wind stress is over-emphasized in the COARE3.5 WBF, especially under moderate wind regimes. Various approaches are explored to alleviate this deficiency by either introducing directional alignment between wind and waves or using the mean wave period instead of the wave period corresponding to the spectral peak to compute the wave age. The findings of this study are likely to be site-dependent, and mostly concern specific regimes of wind and waves where the original parameterization was deficient.
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- PAR ID:
- 10450614
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Oceans
- Volume:
- 128
- Issue:
- 3
- ISSN:
- 2169-9275
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2022JC019277
