Strong and weak constraint variational assimilations for reduced order fluid flow modeling

JOURNAL OF COMPUTATIONAL PHYSICS

卷:231

Article

Artana, G.³  Cammilleri, A.³  Carlier, J.²  Memin, E.¹ 

[1] INRIA, F-35042 Rennes, France

[2] Cemagref Res Ctr, Rennes, France

[3] Fac Ingn UBA, Lab Fluidodinam, Buenos Aires, DF, Argentina

关键词: POD;    Variational assimilation;    Reduced order dynamical systems;    PIV;    Wake flow;   

DOI  :  10.1016/j.jcp.2012.01.010

来源: Elsevier

【 摘 要 】

In this work we propose and evaluate two variational data assimilation techniques for the estimation of low order surrogate experimental dynamical models for fluid flows. Both methods are built from optimal control recipes and rely on proper orthogonal decomposition and a Galerkin projection of the Navier Stokes equation. The techniques proposed differ in the control variables they involve. The first one introduces a weak dynamical model defined only up to an additional uncertainty time-dependent function whereas the second one, handles a strong dynamical constraint in which the dynamical system's coefficients constitute the control variables. Both choices correspond to different approximations of the relation between the reduced basis on which is expressed the motion field and the basis components that have been neglected in the reduced order model construction. The techniques have been assessed on numerical data and for real experimental conditions with noisy particle image velocimetry data. (C) 2012 Elsevier Inc. All rights reserved.

【 授权许可】

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