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

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.