Results 221 to 230 of about 6,618 (260)

On The Hidden Beauty of the Proper Orthogonal Decomposition

Theoretical and Computational Fluid Dynamics, 1991
The proper orthogonal decomposition theorem (Loeve (1955)) of probability theory has been proposed and adapted by Lumley (1967) for detecting spatial coherent patterns in turbulent flows. More specifically, the decomposition extracts deterministic functions from second order statistics of a random field and converges optimally fast in the quadratic ...
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A few thoughts on proper orthogonal decomposition in turbulence

Physics of Fluids, 2017
Proper orthogonal decomposition was originally introduced in turbulence to identify large-scale patterns in turbulent flows. Over the years, several extensions have been formulated in order to strengthen its model-predictive abilities, with limited success in the case of fully developed turbulence.
Podvin, Bérengère, Fraigneau, Yann
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Kosambi and proper orthogonal decomposition

Resonance, 2011
In 1943 Kosambi published a paper titled ‘Statistics in function space’ in the Journal of the Indian Mathematical Society. This paper was the first to propose the technique of statistical analysis often called proper orthogonal decomposition today. This article describes the contents of that paper and Kosambi’s approach to the subject.
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ECL BENCHMARK: APPLICATION OF THE PROPER ORTHOGONAL DECOMPOSITION

Mechanical Systems and Signal Processing, 2003
The aim of this paper is to apply the proper orthogonal decomposition method to a non-linear system consisting of an experimental cantilever beam with a geometrical non-linearity.
Lenaerts, Vincent, Kerschen, Gaëtan, Golinval, Jean-Claude   +2 more
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Proper Orthogonal Decomposition of Turbulent Channel Flow

2001
The technique of the Proper Orthogonal Decomposition (POD) is applied to the case of the turbulent channel flow at Re = 40.000 (based on channel half width). The turbulent flow database is obtained with the use of a finite volume computational code for the numerical integration of the Navier-Stokes equations; for turbulence modeling, the LES approach ...
Alfonsi G., Primavera L., Passoni G., Restano C.   +3 more
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Proper orthogonal decomposition of roof pressure

Journal of Wind Engineering and Industrial Aerodynamics, 1993
Abstract This paper illustrates application of the proper orthogonal decomposition in an investigation of the aerodynamic loading on a roof of a low-rise building. Pneumatically averaged pressure in a corner region of the roof, measured for concerning wind, was used in the analysis.
B. Bienkiewicz, H.J. Ham, Y. Sun
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Observations on the Proper Orthogonal Decomposition

1992
The Proper Orthogonal Decomposition (P.O.D.), also known as the Karhunen-Loeve expansion, is a procedure for decomposing a stochastic field in an L2 optimal sense. It is used in diverse disciplines from image processing to turbulence. Recently the P.O.D.
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Proper orthogonal decomposition of atomistic flow simulations

Journal of Computational Physics, 2012
An adaptive proper orthogonal decomposition based on time windows (WPOD) for analysis of velocity fields from atomistic simulations is presented. The method effectively separates the field into ensemble average and fluctuation components, and can be applied to both stationary and non-stationary flows in simple and complex geometries.
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Proper orthogonal decomposition of DLA clusters

Europhysics Letters (EPL), 1996
We use the proper orthogonal decomposition to analyze the fluctuations around the mean of DLA clusters grown in a sector. These fluctuations are described as a superposition of orthogonal modes, which appear to have very well-defined shapes. These modes are invariants of the growth, and show a strong selection phenomenon which is reflected in the large-
J Elezgaray, F Tallet
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On the optimality of the proper orthogonal decomposition and balanced truncation

2008 47th IEEE Conference on Decision and Control, 2008
The proper orthogonal decomposition (POD), also known as Karhunen-Loeve decomposition or principal component analysis, and balanced truncation, are shown to be optimal in the sense of distance minimizations in spaces of Hilbert-Schmidt or trace-class 2 integral operators. Both POD and balanced truncation are shown to be optimal approximations by finite
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