Turbulent and wave kinetic energy budgets in the airflow over wind-generated surface waves

Wind gustiness in the marine atmospheric boundary layer affects significantly the dynamics of air–sea interaction. To understand the impacts of wind gust events, we perform large-eddy simulation of wind turbulence over a travelling wave to investigate the response of the wind field to an impulsive wind speed increase or decrease. It is found that the turbulence fluctuations and the terms in the turbulent kinetic energy budget equation have a delayed response to the change in the mean flow, while the response of the wave-coherent motions is quasi-stationary. The wave-coherent motions are investigated quantitatively through comparison with a viscous curvilinear model developed by Cao et al. (J. Fluid Mech., vol. 901, 2020, A27) and Cao & Shen (J. Fluid Mech., vol. 919, 2021, A38). We observe an asymmetric hysteresis between the growing wind and the decaying wind in the evolution of the form drag and the viscous drag. We find further that the variation of the wave growth rate during the wind gust is related closely to the contribution from the out-of-phase component of the vertical velocity. Our discoveries provide evidence for the necessity of improving non-equilibrium turbulence and wind input modelling to account for the wind gustiness effect in future studies.

Section: Resultssupporting

confidence: 83%

Large-eddy simulation of gusty wind turbulence over a travelling wave

Hao

Shen

2022

“…where q is the TKE, taking a value of (u u + v v + w w )/2 with v v = (u u + w w )/2 (Yousefi et al 2021). The data show similar magnitudes for the vertical advection A 1 and the horizontal advection A 2 (figure 16).…”

Section: Advection Termmentioning

confidence: 78%

“…The needed spatial resolution is typically smaller than what PIV can offer while maintaining a field of view (FOV) large enough to resolve different wave phases. Therefore, the turbulent dissipation rate is often estimated as a part of the residual term in TKE budget analysis (Yousefi et al 2021). This approach presents challenges in a complete analysis of TKE budget, as the residual includes measurement uncertainty and other non-resolvable terms such as pressure transport.…”

Section: Introductionmentioning

confidence: 99%

“…Further analysis by Buckley & Veron (2019) dissected TKE production, emphasizing significant TKE production linked to Reynolds normal stress in the horizontal direction and the horizontal velocity gradients for young waves (). Using triple-decomposition, Yousefi, Veron & Buckley (2021) examined the TKE production and the production of kinetic energy associated with wave-induced velocity field. Their analysis on the wave–turbulence interaction term indicated the mechanism of energy transfer from wave-induced flow to the turbulence field, particularly for young waves.…”

Section: Introductionmentioning

confidence: 99%

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Turbulence over young wind waves dominated by capillaries and micro-breakers

Do,

Wang,

Chang

2024

We conducted experiments in a laboratory to study turbulent flow over wind generated water waves. The experiments were performed in a wind-wave-current flume with three free stream wind speeds of Uref = 6.0, 8.0 and 10.0 m s−1, corresponding to 10 m equivalent wind speed of U10 = 10.2, 12.2 and 14.1 m s−1 and the root-mean-square wave height of 0.7, 1.1 and 1.7 cm, respectively, at a fetch of 6.2 m. The instantaneous velocity fields above the waves were obtained by using a particle image velocimetry (PIV) technique. The velocity fields were decomposed into the mean, wave-induced and turbulent velocity components. The tested wind waves were primarily dissipated by capillaries and microscale breaking waves. The Bond number and the shear velocity-fetch based Reynolds number were found to correlate with the wind wave regimes well. The turbulent dissipation rates above the water surface were determined based on resolved spatial gradient of instantaneous velocities, where the time-averaged dissipation rate values were calibrated using those estimated from the one-dimensional velocity spectrum in the temporal space. Subsequently, the turbulent kinetic energy (TKE) budget including its production, dissipation, advection and turbulent transport was presented. In addition, conditional averaging analysis of the TKE budgets over leeward, windward sides and all phases was performed. The results showed a strong dependency with the wave phase in the TKE budget terms except for the dissipation. The production-dissipation ratio increased significantly as the wind speed increased, likely attributed to the increased roughness over the substantial coverage of micro-breaking waves.

“…2013; Grare, Lenain & Melville 2018; Hao & Shen 2019; Husain et al. 2019; Yousefi, Veron & Buckley 2020, 2021; Aiyer, Deike & Mueller 2023, among others). These studies have shown that the drag at the ocean surface strongly depends on environmental conditions and is a function of not only wind speed but also wave height, wave slope, wind–wave alignment and wave age, which is defined as the ratio of the wave phase speed to the reference wind speed measured at a height of 10 m above the air–water interface.…”

Section: Introductionmentioning

confidence: 99%

A machine learning model for reconstructing skin-friction drag over ocean surface waves

Yousefi,

Hora,

Yang

et al. 2024

In order to improve the predictive abilities of weather and climate models, it is essential to understand the behaviour of wind stress at the ocean surface. Wind stress is contingent on small-scale interfacial dynamics typically not directly resolved in numerical models. Although skin friction contributes considerably to the total stress up to moderate wind speeds, it is notoriously challenging to measure and predict using physics-based approaches. This work proposes a supervised machine learning (ML) model that estimates the spatial distribution of the skin-friction drag over wind waves using solely wave elevation and wave age, which are relatively easy to acquire. The input–output pairs are high-resolution wave profiles and their corresponding surface viscous stresses collected from laboratory experiments. The ML model is built upon a convolutional neural network architecture that incorporates the Mish nonlinearity as its activation function. Results show that the model can accurately predict the overall distribution of viscous stresses; it captures the peak of viscous stress at/near the crest and its dramatic drop to almost null just past the crest in cases of intermittent airflow separation. The predicted area-aggregate skin friction is also in excellent agreement with the corresponding measurements. The proposed method offers a practical pathway for estimating both local and area-aggregate skin friction and can be easily integrated into existing numerical models for the study of air–sea interactions.