NDLI: Completion of partially known turbulent flow statistics

Content Provider	IEEE Xplore Digital Library
Author	Zare, A. Jovanovic, M.R. Georgiou, T.T.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA (Zare, A.; Jovanovic, M.R.; Georgiou, T.T.)
Abstract	Second-order statistics of turbulent flows can be obtained either experimentally or via high fidelity numerical simulations. The statistics are relevant in understanding fundamental flow physics and for the development of low-complexity models. For example, such models can be used for control design in order to suppress or promote turbulence. Due to experimental or numerical limitations it is often the case that only partial flow statistics are known. In other words, only certain correlations between a limited number of flow field components are available. Thus, it is of interest to complete the statistical signature of the flow field in a way that is consistent with the known dynamics. Our approach to this inverse problem relies on a model governed by stochastically forced linearized Navier-Stokes equations. In this, the statistics of forcing are unknown and sought to explain the given correlations. Identifying suitable stochastic forcing allows us to complete the correlation data of the velocity field. While the system dynamics impose a linear constraint on the admissible correlations, such an inverse problem admits many solutions for the forcing correlations. We use nuclear norm minimization to obtain correlation structures of low complexity. This complexity translates into dimensionality of spatio-temporal filters that can be used to generate the identified forcing statistics.
Starting Page	1674
Ending Page	1679
File Size	1126012
Page Count	6
File Format	PDF
ISBN	9781479932726
ISSN	07431619
e-ISBN	9781479932740
DOI	10.1109/ACC.2014.6859504
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Correlation Mathematical model Covariance matrices Equations Numerical simulation Optimization Null space turbulence modeling Convex optimization flow control low-rank approximation stochastically forced Navier-Stokes equations structured matrix completion problems
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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