Bulletin of the American Physical Society
75th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 20–22, 2022; Indiana Convention Center, Indianapolis, Indiana.
Session S01: Poster Session & Refreshment Break IV (3:22 - 4:10 p.m.)
Monday, November 21, 2022
Room: Hall HI
Abstract: S01.00005 : Estimation of surface viscous stress from wave profiles using deep neural networks*
Gurpreet Singh Hora
(University of Delaware)
(Department of Civil Engineering and Engineering Mechanics, C)
Marco G Giometto
Here, we present a supervised machine learning model to estimate the skin-friction drag of wind waves only from wave profiles and 10 m wind speeds, which are relatively easy to acquire. The input-output pairs are high-resolution wave profiles and their corresponding surface viscous stresses collected from experiments. The model consists of several convolutional neural network blocks with non-linear activation functions. Results show that the model can accurately predict the overall distribution of viscous stress; it captures the peak of viscous stress at/near the crest and its dramatic drop to almost null just past the crest, which is a robust indicator of airflow separation.
*This research is supported by the Columbia University Summer at SEAS Program. H.Y. acknowledges travel funds support from the Department of Civil Engineering and Engineering Mechanics at Columbia University. K.Y. acknowledges support from the National Science Foundation (NSF) under grant number 2030859 to the Computing Research Association (CRA) for the Computing Innovation Fellows Project.
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