Jansen, Kenneth CDS&E_Lighning_2018_NSF_PI_MeetingJansen <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> Turbulence modeling;Data Driven;Machine learning;Multi-fidelity modeling;DNS;RANS;Scale-resolving simulations;NSF-SI2-2018-Talk;Computational Fluid Dynamics 2018-04-23
    https://figshare.com/articles/presentation/CDS_E_Lighning_2018_NSF_PI_MeetingJansen/6174065
10.6084/m9.figshare.6174065.v1