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When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
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Dynamic mode decomposition based predictive model performance on supersonic and transonic aero-optical wavefront

Benjamin D Shaffer, Austin J McDaniel, Christopher C Wilcox

    Applied Optics
    |October 6, 2021
    PubMed
    Summary

    Predictive adaptive optics using dynamic mode decomposition (DMD) improve laser beam correction in turbulent airflow. This method reduces wavefront distortion by up to 25.4%, enhancing directed energy system performance.

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    Area of Science:

    • Aerospace Engineering
    • Optical Physics
    • Fluid Dynamics

    Background:

    • Airborne directed energy systems face performance degradation due to air density variations causing wavefront distortions.
    • Existing adaptive optics (AO) systems struggle with latency, limiting their effectiveness against rapidly evolving aero-optical aberrations.
    • Predictive AO control offers a promising solution by using future state predictions to mitigate these limitations.

    Purpose of the Study:

    • To apply dynamic mode decomposition (DMD) for predicting wavefront distortions in airborne directed energy systems.
    • To evaluate the effectiveness of DMD-based predictive AO in mitigating aero-optical aberrations.
    • To demonstrate improvements in wavefront correction accuracy and potential for enhanced laser system performance.

    Main Methods:

    • Utilized dynamic mode decomposition (DMD), a lightweight algorithm for spatiotemporal pattern analysis.
    • Applied DMD to wavefront data from turbulent boundary layer flow in supersonic and transonic wind tunnels.
    • Developed a predictive model using DMD to forecast future wavefront states from current measurements.

    Main Results:

    • DMD successfully isolated physically meaningful spatiotemporal modes and their dynamics from turbulent flow data.
    • Simulated wavefront correction using DMD showed significant improvements compared to a latency model.
    • Achieved up to a 25.4% reduction in residual wavefront distortion (root mean square over the aperture).

    Conclusions:

    • Dynamic mode decomposition is an effective method for predicting aero-optical distortions in airborne systems.
    • Predictive AO control utilizing DMD can substantially improve wavefront correction accuracy.
    • The demonstrated improvements suggest a pathway to higher laser system performance in challenging atmospheric conditions.