TrAC Seminar Series – John Evans

Title: Data-Driven Turbulence Modeling and Simulation

Abstract

Turbulent fluid flows are characterized by a wide spectrum of spatial and temporal scales.
Unfortunately, the cost of resolving these scales with Direct Numerical Simulation (DNS) grows
quickly with Reynolds number, so engineers will be unable to apply DNS to aerodynamic flows of
industrial interest for many decades to come. Alternatively, one can model all scales using Reynolds
Averaged Navier-Stokes (RANS) or just the smallest scales using Large Eddy Simulation (LES). RANS
remains the turbulence modeling and simulation paradigm of choice in industry while LES continues
to grow in popularity. However, state-of-the-art RANS and LES approaches are inaccurate for many
aerodynamic flows of industrial interest, especially those exhibiting flow separation or transition to
turbulence.
In this talk, I will discuss our work toward arriving at improved RANS and LES approaches by
leveraging advances in machine learning and the availability of high-fidelity simulation data for model
training. The key to our approach is constructing model forms with embedded invariance properties.
This enables us to train remarkably accurate, efficient, and generalizable RANS and LES models using
sparse training data. Specifically, I will provide a high-level overview of our approach as well as
illustrative numerical results. I will also highlight ongoing and future research directions including
Hybrid RANS/LES modeling of separating turbulent boundary layers and in situ learning of
turbulence closures from streaming simulation data.

Bio

Dr. John Evans is an Associate Professor, the Associate Chair for Undergraduate Curriculum, and the
Jack Rominger Faculty Fellow in the Ann and H.J. Smead Department of Aerospace Engineering
Sciences at the University of Colorado Boulder. His research interests lie at the intersection of
computational mechanics, geometry, and approximation theory, with current thrusts in isogeometric
analysis, immersogeometric analysis, interactive simulation, and data-driven modeling. He has won a
number of awards for his research and teaching including the 2021 Gallagher Young Investigator
Award from the United States Association for Computational Mechanics and the 2021 AIAA Rocky
Mountain Educator of the Year (College/University), and he is currently Editor of the journal
Engineering with Computers.