FN Archimer Export Format
PT J
TI Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models
BT 
AF Resseguier, Valentin
   Picard, Agustin M.
   Memin, Etienne
   Chapron, Bertrand
AS 1:1;2:1;3:2;4:3;
FF 1:;2:;3:;4:PDG-ODE-LOPS-SIAM;
C1 Lab, SCALIAN DS, Rennes, France
   Fluminance team, Inria, Rennes, France
   LOPS, Ifremer, Plouzané, France
C2 SCALIAN, FRANCE
   INRIA, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   copubli-france
IF 2.089
TC 5
UR https://archimer.ifremer.fr/doc/00724/83581/88589.pdf
LA English
DT Article
DE ;fluid dynamics;reduced order model;uncertainty quantification;stochastic closure;proper orthog-onal decomposition
AB In this paper, we present a new method to quantify the uncertainty introduced by the drastic dimensionality reduction commonly practiced in the field of computational fluid dynamics, the ultimate goal being to simulate accurate priors for real-time data assimilation. Our key ingredient is a stochastic Navier--Stokes closure mechanism that arises by assuming random unresolved flow components. This decomposition is carried out through Galerkin projection with a proper orthogonal decomposition (POD-Galerkin) basis. The residual velocity fields, model structure, and evolution of coefficients of the reduced order's solutions are used to compute the resulting multiplicative and additive noise's correlations. The low computational cost of these consistent correlation estimators makes them applicable to the study of complex fluid flows. This stochastic POD-reduced order model (POD-ROM) is applied to 2-dimensional and 3-dimensional direct numerical simulations of wake flows at Reynolds 100 and 300, respectively, with uncertainty quantification and forecasting outside the learning interval being the main focus. The proposed stochastic POD-ROM approach is shown to stabilize the unstable temporal coefficients and to maintain their variability under control, while exhibiting an impressively accurate predictive capability. 
PY 2021
SO Siam-asa Journal On Uncertainty Quantification
SN 2166-2525
PU Society for Industrial & Applied Mathematics (SIAM)
VL 9
IS 3
UT 000717476500008
BP 1152
EP 1183
DI 10.1137/19M1354819
ID 83581
ER

EF