2004
DOI: 10.1103/physreve.69.026304
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Low-dimensional model of a supersonic rectangular jet

Abstract: The proper orthogonal decomposition method is applied to the analysis of particle image velocimetry data obtained for a supersonic rectangular jet operated at underexpanded conditions. Phase-locked velocity field data were used to calculate the eigenfunctions and the eigenvalues. It was found that a large fraction of the total energy is contained within the first two modes. The essential features of the jet are thus captured with only two functions. A low-dimensional model for the dynamical behavior is then co… Show more

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Cited by 40 publications
(20 citation statements)
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“…The technique has been successfully applied to a variety of practical flow problems, including boundary layers, 26 turbulent jets, 27 as well as compressible flows. 28 PIV data are particularly suited for POD analyses since the entire spatial velocity field is available, leading to the construction of global eigenmodes.…”
Section: Introductionmentioning
confidence: 99%
“…The technique has been successfully applied to a variety of practical flow problems, including boundary layers, 26 turbulent jets, 27 as well as compressible flows. 28 PIV data are particularly suited for POD analyses since the entire spatial velocity field is available, leading to the construction of global eigenmodes.…”
Section: Introductionmentioning
confidence: 99%
“…It has been applied to a variety of fluid dynamic problems of practical interest, including boundary layers (Aubry et al 1988) and compressible flows (Moreno et al 2004), to produce low-dimensional representations of the flow phenomena present. Particle image velocimetry (PIV) data are particularly suited for the POD analyses, since the entire spatial velocity field is available by snapshots, leading to the construction of global eigenmodes that can be used to further characterize the instantaneous spatial organization of the flow.…”
Section: Introductionmentioning
confidence: 99%
“…An energy-based inner product is preferred because it retains the stable equilibrium points at the origin in the low-dimensional model [9] . Applying this method to flows at moderately high Mach numbers has generated satisfactory results [7] . Eqs.…”
Section: The Pod Vector and Inner Productmentioning
confidence: 92%
“…Thus, the inner product could lose its direct physical interpretation, since velocity and temperature cannot be simply added together. Even if we do not try to make physical sense of the inner product, there remains the question of how to combine kinetic and thermodynamic variables such that the main features of the flow are captured efficiently [7] . Lumley et al [8] introduced a form of inner product, which added normalized velocity and density fluctuations in an optimal way (hereafter referred to as a normalized inner product).…”
Section: Introductionmentioning
confidence: 99%