A machine learning model for reconstructing skin-friction drag over ocean surface waves
Date
2024-03-25
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Fluid Mechanics
Abstract
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.
Description
This article was originally published in Journal of Fluid Mechanics. The version of record is available at: https://doi.org/10.1017/jfm.2024.81. © The Author(s), 2024. Published by Cambridge University Press.
Keywords
machine learning, surface gravity waves, wind-wave interactions
Citation
Yousefi, Kianoosh, Gurpreet Singh Hora, Hongshuo Yang, Fabrice Veron, and Marco G. Giometto. “A Machine Learning Model for Reconstructing Skin-Friction Drag over Ocean Surface Waves.” Journal of Fluid Mechanics 983 (March 25, 2024): A9. https://doi.org/10.1017/jfm.2024.81.