Project title: Physics-based Deep Learning Models for Turbulent Flows with Applications to Renewable Energy
PI: Justin Sirignano
Fluid flow simulation is integral to modelling various physical systems, but the computational demands associated with turbulence modelling remain a challenge in applications such as wind turbine simulations. Improvements in modelling can hence greatly enhance the economic feasibility of wind-energy projects. The proposal aims to develop a machine learning-based model which will provide a low-computational cost, high-accuracy simulation method for improved turbine design and increased renewable power generation.
