The prediction of turbulent flow is a long standing problem in science and engineering. Some of the applications are flow over aircraft wings, combustion in automobile engines, blood flow in arteries, magnetic confinement in fusion, climate modeling, cosmic structure formation.
Right: Computation of the trailing vortex of an aircraft (Courtesy of K. Duraisamy)
|Framework for Turbulence Modeling using Big Data (PI: K. Duraisamy)
Developing the science behind data-driven turbulence modeling and demonstrate the utility of large-scale data-driven techniques in turbulence modeling.It involves the development of domain-specific learning techniques suited for the representation of turbulence and its modeling, the establishment of a trusted ensemble of data for the creation and validation of new models, and the deployment of these models in complex aerospace problems. This is a collaborative effort between the University of Michigan, Stanford University, Iowa State and Pivotal Inc., consulting with Boeing Commerical Airplanes and interacting with NASA Langley Research Center.
Funded by the LEARN (Leading Edge Aeronautics Research for NASA) program, through the NASA Aeronautics Research Institute (NARI)
|Formalisms and Tools for Data-driven Turbulence Modeling (PI: K. Duraisamy)
Devising rigorous mathematical techniques that utilize large databases obtained by simulations to develop predictive models of turbulent flow. The tools are of general applicability for data-driven modeling in all areas of science and engineering.
Funded by NSF