Proper-orthogonal decomposition of spatio-temporal patterns in fluidized beds

Cizmas, Paul G. A.; Palacios, Antonio Lozano; O’Brien, Thomas J.; Syamlal, Madhava

doi:10.1016/s0009-2509(03)00323-3

Cited by 69 publications

(24 citation statements)

References 18 publications

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“…A general mathematical description of the method can be found in [11], while details regarding practical numerical implementations are given, for example, in [11,12]. Applications of this method range from the study of spatio-temporal dynamics in electrochemical [3,4,13], catalytic [14,15], and reaction-diffusion systems [16][17][18], flames [19], and hydrodynamic systems [20]. KL decomposition 89 has been used to characterize spatio-temporal chaos [21] and to perform model identification [22] as well.…”

Section: A Karhunen-loève (Kl) Decompositionmentioning

confidence: 99%

Complex spatiotemporal dynamics in metal electrodissolution: three dimensional cell geometry models.

Bı̂rzu¹

2012

JCIS

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Complex dynamics of current distribution on ring or disk working electrodes is studied numerically by modelling metal electrodissolution under potentiostatic control in a three dimensional electrochemical cell. Such spatially distributed systems exhibit extremely rich dynamics in the oscillatory regime, ranging from rotating pulses to spatio-temporal chaos. Several dynamical behaviors were selected and characterized using the Karhunen-Loève (KL) decomposition and the analytical signal approach.

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Section: A Karhunen-loève (Kl) Decompositionmentioning

confidence: 99%

Complex spatiotemporal dynamics in metal electrodissolution: three dimensional cell geometry models.

Bı̂rzu¹

2012

JCIS

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“…P.G. Cizmas et al(2003) used POD to simulate a laboratory scale fluidized bed. The model consists of four PDEs in the rectangular field (2-D).…”

Section: Applications Of Pod In Literaturementioning

confidence: 99%

“…Otherwise, the boundary condition and Dirichlet conditions are very difficult to formulate; consequently, Galerkin projection can not be used in converting PDE to ODE which is crucial for obtain the coefficients of POD modes. (Cizmas, 2003). Only one or two properties, density and viscosity, were considered in the model and these are kept constant.…”

Section: Restrictions Of Podmentioning

confidence: 99%

Software Framework for Advanced Power Plant Simulations

Widmann¹,

Munteanu²,

Jain³

et al. 2010

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This report summarizes the work accomplished during the Phase II development effort of the Advanced Process Engineering Co-Simulator (APECS). The objective of the project is to develop the tools to efficiently combine high-fidelity computational fluid dynamics (CFD) models with process modeling software. During the course of the project, a robust integration controller was developed that can be used in any CAPE-OPEN compliant process modeling environment. The controller mediates the exchange of information between the process modeling software and the CFD software. Several approaches to reducing the time disparity between CFD simulations and process modeling have been investigated and implemented. These include enabling the CFD models to be run on a remote cluster and enabling multiple CFD models to be run simultaneously. Furthermore, computationally fast reduced-order models (ROMs) have been developed that can be "trained" using the results from CFD simulations and then used directly within flowsheets. Unit operation models (both CFD and ROMs) can be uploaded to a model database and shared between multiple users.

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“…13-20. Additional results have been presented in [1]. The algorithm for performing the POD decomposition was also tested on a complex flow with large unsteadiness, such as the flow in turbomachinery cascades.…”

Section: Algorithm For Pod Basis Functions Calculationmentioning

confidence: 99%

A Reduced Order Model of Two-Phase Flow, Heat Transfer and Combustion in Circulating Fluidized-Beds

Cizmas¹

2003

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The report summarizes the objectives, tasks and accomplishments of this research project. The report presents the following program deliverables: (1) database generation using MFIX code, (2) development and implementation of an algorithm to calculate the proper orthogonal decomposition (POD) basis functions, (3) visualization tools for reconstructing simulated data, (4) algorithms for reducing the partial differential equations to ordinary differential equations, (5) visualization tools for Galerkin ordinary differential equations, (6) verification and validation of the code by comparing POD and conventional solution results, and (7) development of POD strategy for best energy cut-off values.3

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Proper-orthogonal decomposition of spatio-temporal patterns in fluidized beds

Cited by 69 publications

References 18 publications

Complex spatiotemporal dynamics in metal electrodissolution: three dimensional cell geometry models.

Complex spatiotemporal dynamics in metal electrodissolution: three dimensional cell geometry models.

Software Framework for Advanced Power Plant Simulations

A Reduced Order Model of Two-Phase Flow, Heat Transfer and Combustion in Circulating Fluidized-Beds

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