Excited to share that our latest paper on developing a supervised machine-learning model to reconstruct skin-friction drag over surface wind waves has been published in the Journal of Fluid Mechanics! Check out the full paper at
doi.org/10.1017/jfm.2024.81. This work is an important milestone for starting our collaborative research (between the University of Texas at Dallas and Columbia University) as part of a recent National Science Foundation (NSF) grant (award number
2404368).
In this work, we proposed an 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 incorporating 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.
