CDS&EPoster_2018_NSF_PI_MeetingJansen

Turbulence evolves through highly nonlinear interactions of a broad spectrum of spatial and temporal scales. Modeling these interactions involves a tradeoff between accuracy and computational cost. Uncertainty quantification and design analyses often require solutions for numerous parameter combinations, rendering Reynolds Averaged Navier-Stokes (RANS) the only feasible option. This work seeks to reduce the cost of accurately predicting separating turbulent boundary layers using data-centric analysis, multi-fidelity modeling, and machine learning.