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arXiv · 2110.15218

Dynamic Mode Decomposition for Aero-Optic Wavefront Characterization

Shervin Sahba·Diya Sashidhar·Christopher C. Wilcox·Austin McDaniel·Steven L. Brunton·J. Nathan Kutz
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Abstract

Aero-optical beam control relies on the development of low-latency forecasting techniques to quickly predict wavefronts aberrated by the Turbulent Boundary Layer (TBL) around an airborne optical system, and its study applies to a multi-domain need from astronomy to microscopy for high-fidelity laser propagation. We leverage the forecasting capabilities of the Dynamic Mode Decomposition (DMD) -- an equation-free, data-driven method for identifying coherent flow structures and their associated spatiotemporal dynamics -- in order to estimate future state wavefront phase aberrations to feed into an adaptive optic (AO) control loop. We specifically leverage the optimized DMD (opt-DMD) algorithm on a subset of the Airborne Aero-Optics Laboratory Transonic (AAOL-T) experimental dataset, characterizing aberrated wavefront dynamics for 23 beam propagation directions via the spatiotemporal decomposition underlying DMD. Critically, we show that opt-DMD produces an optimally de-biased eigenvalue spectrum with imaginary eigenvalues, allowing for arbitrarily long forecasting to produce a robust future-state prediction, while exact DMD loses structural information due to modal decay rates.

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Shervin Sahba, Diya Sashidhar, Christopher C. Wilcox, Austin McDaniel, Steven L. Brunton, J. Nathan Kutz. 2021-09-28. Dynamic Mode Decomposition for Aero-Optic Wavefront Characterization. https://doi.org/10.1117/1.oe.61.1.013105

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