Higher order dynamic mode decomposition to model reacting flows

Higher Order Dynamic Mode Decomposition

Domain Decomposition Methods for . . . of Reacting Flows

Domain decomposition is a natural route to parallel computing for partial differential equation solvers. In this procedure, subdomains of which the original domain of definition is comprised are assigned to independent processors at the price of periodic coordination between processors to compute global parameters and maintain the requisite degree of continuity of the solution at the subdomain ...

Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics

A hybrid denoising method is presented as a combination of Empirical Mode Decomposition (EMD) and Higher Order Statistics (HOS). EMD, an adaptive data-driven method, is used for effective decomposition of a noisy signal into its functional components. Then Kurtosis and Bispectrum operate as Gaussianity estimators, supplemented by Bootstrap techniques, ensuring detection and removal of the signa...

Dynamic Optimization of Chemically Reacting Stagnation Flows

This paper presents a dynamic-optimization algorithm that can be used to minimize a generalized objective function. It is demonstrated for a chemical-vapor-deposition (CVD) process in a stagnation-flow reactor. The equations describing the chemically reacting flow are written in a transient compressible similarity form. After discretizing the spatial derivatives on a finite-volume mesh, the sys...

Randomized Dynamic Mode Decomposition

This paper presents a randomized algorithm for computing the near-optimal low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging techniques to compute low-rank matrix approximations. They are able to ease the computational challenges arising in the area of ‘big data’. The idea is to derive from the high-dimensional input matrix a smaller matrix, which is then used to effi...