Learning the Dynamics of Surface Waves - Airflow Interaction

The momentum exchanges between the atmosphere and ocean are strongly contingent on small-scale interfacial dynamics. They are crucial for wave prediction models, climate modeling, and weather forecasting, which significantly impact many aspects of human life. To improve the current wave modeling capabilities, it is essential to understand the behavior of wind stress at the air-sea interface, which is the sum of skin friction and form drag. In this project, we aim to use different tools in machine learning to develop a sea-state-dependent model for skin friction and form drag and accurately predict the small-scale turbulence near the surface. The machine learning algorithm will be trained on unique experimental and field data for different wind-wave conditions. In this dataset, the tangential stress is measured directly and the form drag (pressure drag) will be obtained using a recently developed pressure field reconstruction technique.

Name of Lab: Environmental Flow Physics Laboratory

Direct Supervisor: Dr. Kianoosh Yousefi (Associate Research Scientist)

Hours per week: 40 hr/week

Position type: Hybrid (both remote and on site)

Position in paid and available for credit 

Qualifications: Machine learning and Python 

Eligibility: Senior, Master's

SEAS students only: No

Kianoosh Yousefi, [email protected]