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>