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Data-driven Modeling and Machine Learning in Fluid Mechanics

1. Data-driven turbulence modeling and uncertainty quantification in numerical simulations of fluid flows.

2. Subgrid scale and wall modeling in LES with physics-constrained machine learning.

3. Wind-farm modeling via physics-informed deep learning. 

4. Data-driven dynamical system modeling. 

Large-Eddy Simulation (LES)

1. Developing and testing new subgrig-scale (SGS) ans wall models for LES of turbulent flows.

2. LES of turbulent flows in comlex geometries such as fluid flows through wind farms, complex terrains, pumps, among others. 

Wind-Farm Modeling, Optimization and Control

1. Wind-farm modeling, optimization and control using CFD, analytical models and physics-informed machine learning.

2. Non-traditional wind turbines and wind farms (e.g., multi-rotor turbines, vertical axis turbines, multiple turbine heights and sizes).

3. Energy harvesting by collocating horizontal and vertical axis wind turbines.

Boundary-Layer Turbulence

1. In-depth understating and characterization of turbulence in the turbulent boundary layer under different thermal stratification.

2. Exploring the structure of secondary motions in thermally-stratified boundary layers.  

Uncertainty Quantification

1. Quantifying the structural uncertainties in Reynolds-averaged Navier-Stokes (RANS) closures in CFD simulations of engineering problems.