10.6084/m9.figshare.6174053.v1 Kenneth Jansen Kenneth Jansen CDS&EPoster_2018_NSF_PI_MeetingJansen figshare 2018 Turbulence multi-fidelity modeling machine learning fluid dynamics DNS RANS Scale-resolving Simulations Turbulence Modeling NSF-SI2-2018 Computational Fluid Dynamics Fluidisation and Fluid Mechanics Aerospace Engineering Mechanical Engineering 2018-04-23 22:28:12 Poster https://figshare.com/articles/poster/CDS_EPoster_2018_NSF_PI_MeetingJansen/6174053 <p>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.</p>