Data-driven modal decomposition techniques for high-dimensional flow fields

Abstract

Data-driven decomposition techniques are presented for the analysis and development of reduced-order models of complex flow dynamics. The Proper Orthogonal Decomposition (POD) produces optimal representations of the dynamics in the sense of the energy norm. Alternatively, Dynamic Mode Decomposition (DMD) efficiently extracts coherent dynamics based on eigendecompositions of linearized dynamics. An extension to the latter, the Higher Order Dynamic Mode Decomposition (HODMD) method uses time delays to develop efficient reduced models to represent complex dynamics in a nonintrusive manner. High-fidelity simulation results of a laboratory-scale single-element gas turbine combustor are used to demonstrate and evaluate the capabilities of the aforementioned decomposition techniques.