Proper orthogonal decomposition and low-dimensional models for driven cavity flows

Proper Orthogonal Decomposition Closure Models for Fluid Flows: Burgers Equation

This paper puts forth several closure models for the proper orthogonal decomposition (POD) reduced order modeling of fluid flows. These new closure models, together with other standard closure models, are investigated in the numerical simulation of the Burgers equation. This simplified setting represents just the first step in the investigation of the new closure models. It allows a thorough as...

Low Dimensional Azimuthal Characteristics of Suddenly Expanding Axisymmetric Flows using Proper Orthogonal and Fourier Decomposition

An intrinsic stabilization scheme for proper orthogonal decomposition based low-dimensional models

Despite the temporal and spatial complexity of common fluid flows, model dimensionality can often be greatly reduced while both capturing and illuminating the nonlinear dynamics of the flow. This work follows the methodology of direct numerical simulation !DNS" followed by proper orthogonal decomposition !POD" of temporally sampled DNS data to derive temporal and spatial eigenfunctions. The DNS...

Proper Orthogonal Decomposition Technique for Transonic Unsteady Aerodynamic Flows

A new method for constructing reduced-order models (ROM) of unsteady small-disturbance  ows is presented. The reduced-order models are constructed using basis vectors determined from the proper orthogonal decomposition (POD) of an ensemble of small-disturbance frequency-domain solutions. Each of the individual frequencydomain solutions is computed using an efŽ cient time-linearized  ow solver...

Linear Models from Proper Orthogonal Decomposition

Proper Orthogonal Decomposition (POD), alternatively known as Principal Component Analysis or the Karhunen-Loève decomposition, is a model-reduction technique which generates the optimal linear subspace of dimension D for a given set of higher-dimensional data. That is, if the data are contained within an attractor, the POD process can produce the affine linear space that best approximates the ...