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Toward neural network-based optical wave reconstruction for the supersonic turbulent cavity flow
Applied Optics
|June 10, 2026
Summary
Researchers developed a data-driven method to correct aero-optical distortions caused by atmospheric density fluctuations. An artificial neural network reconstructs wavefronts, offering faster, more accurate imaging error correction for supersonic and hypersonic applications.
Area of Science:
- Fluid dynamics
- Wavefront sensing
- Computational physics
Background:
- Aero-optical distortions, caused by atmospheric density fluctuations, lead to bore-sight errors in optical systems.
- Existing correction methods are limited for supersonic and hypersonic flows.
Purpose of the Study:
- To develop and validate a data-driven approach for correcting aero-optical distortions.
- To investigate wavefront aberrations in supersonic shear layers over cavities.
Main Methods:
- Large-eddy simulation of supersonic flow (Mach 2.3) over a cavity at 16 km altitude using the JENRE Multiphysics Framework.
- Calculation of optical path difference (OPD) from high-frequency density data.
- Spectral proper orthogonal decomposition (SPOD) to identify dominant flow structures.
- Training an artificial neural network (ANN) to reconstruct wavefronts from simulated Shack-Hartmann sensor data.
Main Results:
- Dominant flow structures contributing to wavefront aberrations were identified via SPOD.
- An ANN was successfully trained to reconstruct the original wavefront from simulated sensor data.
- The data-driven approach showed potential for rapid and precise correction of imaging errors.
Conclusions:
- The study presents a viable data-driven method for correcting aero-optical distortions in supersonic flows.
- Artificial neural networks offer a promising alternative to traditional methods for real-time wavefront correction.
- This approach can be tailored for specific operational conditions to enhance imaging performance.
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