skip to main content

AOE Home / About / AOE seminars / Archive / 2016 

Physics-Informed Machine Learning for Predictive Modeling of Turbulent Flows

  • November 7, 2016
  • Dr. Heng Xiao
  • Virginia Tech, Department of Aerospace and Ocean Eng.
  • 117A Surge Building
  • 4:00 p.m.
  • Faculty Host: Dr. Rakesh Kapania

Abstract: Many complex systems are characterized by physics at a wide range of scales, for which first-principle-based high-fidelity models resolving all the scales are prohibitively expensive to run. Consequently, practical simulations have primarily relied on low-fidelity models with approximate closure models, which introduce large model-form uncertainties and diminish their predictive capabilities. Turbulent flows are a classical example of such complex physical systems, where numerical solvers with turbulence closure models are widely used in industrial flow simulations.  In light of the decades-long stagnation in traditional turbulence modeling, data-driven methods have been proposed as a promising alternative.  We present a comprehensive framework for using existing data to reduce model uncertainties in turbulent flow simulations.  While the focus is on turbulent flows, the framework is general enough for other complex physical systems.

Tags