Examination of a LSE/POD complementary technique using single and multi-time information in the axisymmetric shear layer

Ewing, D.; Citriniti, Joseph H.

doi:10.1007/978-94-011-4601-2_33

Cited by 42 publications

(40 citation statements)

References 3 publications

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“…In order to improve estimation performance, extensions of the above methods have been proposed: quadratic stochastic estimation (QSE) [1], [13] and spectral linear stochastic estimation (SLSE) [7]. They allow more accurate estimations compared with LSQ or LSE methods, but, in fact, neither of these methods takes into account the underlying dynamic model that the POD coefficients must satisfy, i.e., a finite dimensional equivalent of the Navier-Stokes equations that is obtained by the Galerkin projection of the flow equations on the POD modes retained for the representation of the velocity field.…”

Section: Introductionmentioning

confidence: 99%

A non-linear observer for unsteady three-dimensional flows

Buffoni

Camarri

Iollo

et al. 2008

Journal of Computational Physics

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A method is proposed to estimate the velocity field of an unsteady flow using a limited number of flow measurements. The method is based on a non-linear low-dimensional model of the flow and on expanding the velocity field in terms of empirical basis functions. The main idea is to impose that the coefficients of the modal expansion of the velocity field give the best approximation to the available measurements and that at the same time they satisfy as close as possible the nonlinear low-order model. The practical use may range from feedback flow control to monitoring of the flow in non-accessible regions. The proposed technique is applied to the flow around a confined square cylinder, both in two-and three-dimensional laminar flow regimes. Comparisons are provided with existing linear and non-linear estimation techniques.

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Section: Introductionmentioning

confidence: 99%

A non-linear observer for unsteady three-dimensional flows

Buffoni

Camarri

Iollo

et al. 2008

Journal of Computational Physics

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show abstract

“…The first operates entirely in the frequency domain and, therefore, it is called spectral linear stochastic estimation. It was introduced by Ewing and Citriniti [4] and has been developed in detail by Tinney et al [5]. The second approach operates in the time domain, and was suggested by Ukeiley et al [6] and later refined and studied in detail by Durgesh and Naughton [7] and Lasagna et al [8].…”

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confidence: 99%

“…This might be the case for certain strongly nonlinear physical processes, in which prediction of the flow state using nonlinear estimators might be required. In this paper we propose two different methods to find nonlinear approximation of the conditional average expressed by equation (4). The first method is a straightforward extension of the linear multi-time-delay technique and it is based on a Volterra series expansion, which is a common approach to model the response on nonlinear systems, in a wide range of research and engineering areas [23].…”

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confidence: 99%

“…To cope with these issues, in this paper we also use Artificial Neural Networks (ANN) to model the nonlinear character of the conditional average expressed in equation (4). Artificial Neural Networks are computational models that can accurately approximate complex nonlinear input/output relationships with unknown structure.…”

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confidence: 99%

“…. , s(t − n∆t)] of equation (4). These are densely connected to the n h nodes in the hidden layer, which includes one bias node.…”

mentioning

confidence: 99%

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