Dynamic mode decomposition based predictive model performance on supersonic and transonic aero-optical wavefront measurements.

Air density variations around an airborne directed energy system distort a beam's wavefront, resulting in degraded performance after propagation into the far field. Adaptive optics (AO) can be used to correct for these rapidly evolving aero-optical aberrations; however, in some conditions, the inherent latency between measurement and correction in state-of-the-art AO systems results in significantly reduced performance. Predictive AO control methods utilize future state predictions to compensate for rapidly evolving distortions and are promising techniques for mitigating this limitation. This paper demonstrates an application of the dynamic mode decomposition (DMD) method on turbulent boundary layer wavefront data from supersonic and transonic wind tunnel flow from the Air Force Research Laboratory's Aero-Effects Laboratory. DMD is a lightweight algorithm used to isolate spatiotemporal patterns in a dataset into physically meaningful modes with associated dynamics, which were used to predict future states from a given wavefront. This method showed notable improvements in simulated wavefront correction, providing a reduction of residual wavefront distortion, measured as root mean square over the aperture, by up to 25.4% over a simulated latency model, which could accordingly result in higher laser system performance.