Data-Driven Modal Decomposition Techniques for High-Dimensional Flow Fields

Arnold-Medabalimi, Nicholas; Huang, Cheng; Duraisamy, Karthik

doi:10.1007/978-3-030-44718-2_7

Cited by 2 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DMD extracts more physical representations of complex spatio-temporal structures than POD by constraining the temporal modes to discrete frequencies. Details of this method and formulation can be found in previous works 68,71 but is summarized here. This family of model decomposition methods begin by organizing the field data into vectors of a snapshot matrix.…”

Section: Dynamic Mode Decompositionmentioning

confidence: 99%

Large-eddy simulation and challenges for projection-based reduced-order modeling of a gas turbine model combustor

Arnold-Medabalimi

Huang

Duraisamy

2022

International Journal of Spray and Combustion Dynamics

Self Cite

View full text Add to dashboard Cite

Computationally efficient modeling of gas turbine combustion is challenging due to the chaotic multi-scale physics and the complex non-linear interactions between acoustic, hydrodynamic, and chemical processes. A large-eddy simulation, referred to as the full order model (FOM), is performed for a gas turbine model combustor with turbulent combustion effects modeled using a flamelet-based method. Modal analysis reveals a high degree of correlation with averaged and instantaneous high-frequency particle image velocimetry fields. The dynamics of the precessing vortex core is quantitatively characterized using dynamic mode decomposition. The governing equations of the FOM are projected onto a low-dimensional linear manifold to construct a reduced-order model (ROM). A discretely-consistent least squares projection is used to guarantee global stability. The ROM provides an accurate reconstruction of the combustion dynamics within the training region, but faces a significant challenge in future state predictions. This limitation is mainly due to the increased projection error, which in turn is a direct consequence of the highly chaotic nature of the flow field, involving a wide range of dispersed coherent structures. This shortcoming is overcome using an adaptive basis method which yields accurate predictions of dynamics beyond the training region consistent with the FOM. Formal projection-based ROMs have not been applied to a problem of this scale and complexity, and achieving accurate and efficient ROMs is a grand challenge problem. A production-ready ROM method will significantly decrease the computational cost of the flame dynamics as well as the portability of this prediction to smaller-scale computers.

show abstract

Section: Dynamic Mode Decompositionmentioning

confidence: 99%