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...

This report focuses on improving the stability as well as the approximation properties of Reduced Order Models (ROM) based on Proper Orthogonal Decomposition (POD). The ROM is obtained by seeking a solution belonging to the POD subspace and that at the same time minimizes the Navier-Stokes residuals. We propose a modified ROM that directly incorporates the pressure term in the model. The ROM is...

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...

There are various techniques available to model the turbulence structure. Turbulence of incompressible water flow (without sediment) can be modelled completely using the Navier-Stokes equations. However for even a small part of a river channel, the size of the problem is many orders of magnitude larger than even the world’s largest super-computers can handle. This means that other techniques ar...

The numerical solution of partial differential equations (PDEs) depending on parametrized or random input data is computationally intensive. Reduced order modeling techniques, such as the reduced basis methods, have been developed to alleviate this computational burden, and are nowadays exploited to accelerate real-time analysis, as well as the solution of PDE-constrained optimization and inver...

Proper orthogonal decomposition (POD) has been widely realized as a tool for compressing fluctuating building pressure data in wind-engineering area. Modes are determined by eigen decomposition of data covariance matrix, and then truncation is applied to retain only the modes with the highest energy. However, as observed by S. Kho, C. Baker, R. Hoxey [Pod/arma reconstruction of the surface pres...

Proper orthogonal decomposition (POD) is a powerful technique for model reduction of linear and non-linear systems. It is based on a Galerkin type discretization with basis elements created from the system itself. In this work, error estimates for Galerkin POD methods for linear elliptic, parameter-dependent systems are proved. The resulting error bounds depend on the number of POD basis functi...

Proper orthogonal decompositions (POD) have been used to systematically extract the most energetic modes while centroidal Voronoi tessellations (CVT) have been used to systematically extract best representatives. We combine the ideas of CVT and POD into a hybrid method for model reduction. The optimality of such an approach and various practical implementation strategies are discussed.

A new method for enhanced surrogate modeling of complex systems by exploiting gradient information is presented. The technique combines the proper orthogonal decomposition (POD) and interpolation methods capable of fitting both sampled input values and sampled derivative information like Kriging (aka spatial Gaussian processes). In contrast to existing POD-based interpolation approaches, the gr...