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SUMMARY:Quantifying Uncertainty in Turbulent Flow Predictions based on RAN
 S/LES Closures - Gianluca Iaccarino\, Stanford University
DTSTART:20180222T140000Z
DTEND:20180222T150000Z
UID:TALK100609@talks.cam.ac.uk
CONTACT:Pranay Seshadri
DESCRIPTION:Despite recent developments in high-fidelity turbulent flow si
 mulations\, Reynolds Averaged Navier-Stokes (RANS) closures  remain broadl
 y used in real-world applications\, due to their inherent low cost. Howeve
 r\, RANS models are based on assumptions (model-form) that are typically d
 ifficult to verify\, leading to potential uncertainty in the predictions. 
 Applying the spectral decomposition to the modeled Reynolds-Stress Tensor 
 (RST) allows for the introduction of decoupled perturbations into the base
 line turbulence intensity (kinetic energy)\, shape (eigenvalues)\, and ori
 entation (eigenvectors) of the stresses. This constitutes a natural method
 ology to evaluate the model form uncertainty associated to different aspec
 ts of RST modeling. In a predictive setting\, one frequently encounters an
  absence of any relevant reference data. To make data-free predictions wit
 h\nquantified uncertainty we employ physical bounds to a-priori define max
 imum spectral perturbations. When propagated\, these perturbations yield c
 onservative intervals with engineering utility. Detailed experiments and h
 igh-fidelity data open up the possibility of inferring a distribution of u
 ncertainty\, by means of various data-driven machine-learning techniques. 
 We will demonstrate our framework on a number of flow problems where RANS 
 models are prone to failure using both the data-free and data-driven appro
 aches. Recent extensions of the same framework to subgrid closures used in
  Large Eddy Simulations (LES) will be briefly described.
LOCATION: Cambridge University Engineering Department LT0
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