Dynamic Mode Decomposition for Plasma Diagnostics and Validation

Abstract

We demonstrate the application of the Dynamic Mode Decomposition (DMD) for the diagnostic analysis of the nonlinear dynamics of a magnetized plasma in resistive magnetohydrodynamics. The DMD method is an ideal spatio-temporal matrix decomposition that correlates spatial features of computational or experimental data while simultaneously associating the spatial activity with periodic temporal behavior. DMD can produce low-rank, reduced order surrogate models that can be used to reconstruct the state of the system and produce high-fidelity future state predictions. This allows for a reduction in the computational cost, and, at the same time, accurate approximations of the problem, even if the data are sparsely sampled. We demonstrate the use of the method on both numerical and experimental data, showing that it is a successful mathematical architecture for characterizing the HIT-IS magnetohydrodynamics. Importantly, the DMD decomposition produces interpretable, dominant mode structures, including the spheromak mode and a pair of injector-driven modes. In combination, the 3-mode DMD model produces excellent dynamic reconstructions that can be used for future state prediction and/or control strategies.

17 pages, 9 figures

References in corpus (2)

• On Dynamic Mode Decomposition: Theory and Applications
• Toward compressed DMD: spectral analysis of fluid flows using sub-Nyquist-rate PIV data

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• Accelerating Particle-in-Cell Kinetic Plasma Simulations via Reduced-Order Modeling of Space-Charge Dynamics using Dynamic Mode Decomposition
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• Fast prediction of plasma instabilities with sparse-grid-accelerated optimized dynamic mode decomposition