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Aero-optical beam control relies on the development of low-latency forecasting techniques to quickly predict wavefronts aberrated by the turbulent boundary layer around an airborne optical system, and its study applies to a multidomain 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 — to estimate future state wavefront phase aberrations to feed into an adaptive optic control loop. We specifically leverage the optimized DMD (opt-DMD) algorithm on a subset of the Airborne Aero-Optics Laboratory-Transonic 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 debiased 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. [ABSTRACT FROM AUTHOR]

The Naval Research Laboratory has developed a method for simulating atmospheric turbulence and a testbed that simulates its aberrations using a liquid crystal (LC) spatial light modulator (SLM). This testbed allows the simulation of so-called atmospheric seeing conditions ranging from very poor to very good and different algorithms may be easily employed on the device for comparison. Many models for simulating turbulence often neglect temporal transitions along with different seeing conditions. Using the statistically independent set of Karhunen-Loeve polynomials in conjunction with Kolmogorov statistics in this approach provides a spatial and temporal model for simulating turbulence. An added benefit to using an LC SLM is its low cost; and multiple devices can be used to simulate multiple layers of turbulence in a laboratory environment. Current testing using multiple LC SLMs is under investigation at the Naval Research Laboratory. [ABSTRACT FROM AUTHOR]

In this work, we characterize the capability of artificial neural network predictive models for generalizable turbulence forecasting, particularly for use in predictive adaptive optics (AO) applications. Predictive AO control, which utilizes future state predictions of an optical wavefront propagated through a turbulent medium to drive correction, is a promising technology for optical propagation in high-disturbance and low-signal environments. The dynamics describing the evolution of turbulent flow can vary greatly. Accordingly, a generalizable approach to turbulence forecasting has key benefits in allowing for prediction across a range of conditions, thus enabling continuous predictive AO operation in dynamic environments and having reduced sensitivity to changes in conditions. We present a model for generalizable turbulence forecasting, which demonstrated consistent high performance over a range of compressible flow conditions outside those included in the training sample, with only a minimal increase in prediction error compared with a hypothetical baseline model, which assumes perfect a priori characterization. These results demonstrate a clear ability to extract useful dynamics from a limited domain of turbulent conditions and apply these appropriately for forecasting, which could inform future design of predictive AO systems.

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.