Experimental investigation into the effects of shock waves on Shack-Hartmann wavefront sensor measurements

Kemnetz, Matthew R.; Kalensky, Matthew; DeFoor, Thomas E.; Spencer, Mark F.

doi:10.1117/12.2676878

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Irradiance pattern spreading caused by shock waves has been observed experimentally on numerous occasions [103,104]. For example, Fig.…”

Section: Shock Wavesmentioning

confidence: 99%

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Aero-optical effects, part II. Sources of aberrations: tutorial

Spencer,

Kalensky,

Gordeyev

et al. 2024

Preprint

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This paper serves as Part II of a two-part tutorial on ``aero-optical effects.'' In Part I, we provide introductory material with an emphasis on system-level considerations, particularly for those who are new to the field of aero-optics. In Part II, we move on to survey several sources of aberrations. For example, we cover foundational sources like boundary layers and shear layers, as well as miscellaneous sources like mechanical contamination, shock waves, and aero acoustics. Throughout Part II, we emphasize drivers for system-level performance, which appropriately builds on the system-level considerations covered in Part I. This emphasis will inform future efforts looking to develop airborne-laser systems flying at subsonic, supersonic, and hypersonic speeds.

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“…Irradiance pattern spreading caused by shock waves has been observed experimentally on numerous occasions [103,104]. For example, Fig.…”

Section: Shock Wavesmentioning

confidence: 99%

“…Experimental SHWFS measurements collected for a laser beam that propagated through a shock wave generated in a supersonic wind-tunnel facility. Adapted from Ref [103]. with permission.…”

mentioning

confidence: 99%

Aero-optical effects, part II. Sources of aberrations: tutorial

Spencer,

Kalensky,

Gordeyev

et al. 2024

Preprint

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Parametric studies of effects of finite pixel and AOI sizes on the accuracy of Shack-Hartmann WFS

Chu,

Gordeyev

2024

Unconventional Imaging, Sensing, and Adaptive Optics 2024

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In previous work, we studied the effects of a supersonic shockwave on the performance of a Shack-Hartmann wavefront sensor (SHWFS). A shockwave is a sudden change in density which then cause a sharp gradient in the wavefront. This results in dots distorting in the SHWFS dot pattern which in turn affects the accuracy of wavefront reconstruction. We used higher order statistics, such as standard deviation, skewness and kurtosis to identify and eliminate these distorted dots in Shack-Hartmann wavefront sensors, improving the accuracy of the final reconstructed wavefront. However, this is contingent on accurate calculations of the centroid, standard deviation, skewness, and kurtosis of these dots. In the first part of this work, we look at the effect of the finite pixel size on the accuracy of the Shack-Hartmann wavefront sensor, specifically on the calculation of the centroid and higher order statistics of a pixelated dot. We simulate a dot and average the intensity distribution for different pixel sizes. We show that the error is significant if the ratio of the center Dot size/pixel size is less than 2. Past this ratio of 2, the centroid and statistics error of the dots drop to near zero. We also explain some striping artifacts observed in the statistics maps in experimental work. In the second part of this paper, we investigate the centroid error of a dot approaching the edge of the Area of Interest (AOI). In cases of large tilts in the wavefronts, the dot can be pushed into or past the edge of the AOI. We show that a large AOI size/Dot size ratio allows for the most tilt and range of movement. However, by squaring or cubing the intensity distribution, the error can be further reduced for a given system.

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Phase discontinuity classification from single-aperture irradiance patterns using machine learning

DeFoor,

Bukowski,

Kalensky

et al. 2024

Unconventional Imaging, Sensing, and Adaptive Optics 2024

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This paper explores the efficacy of employing machine learning, specifically an encoder-style convolutional neural network, to estimate the magnitude of an optical-phase discontinuity (|∆ϕ|) that results in an aberrated, farfield irradiance pattern. The model receives a single 32×32 normalized irradiance pattern image and returns the estimated |∆ϕ|. We train and validate the model using simulated data with varying values of ∆ϕ (from 0 to 2π radians), discontinuity locations within the aperture of the simulated system, and strengths of background noise. In exploring this trade space, we calculate the mean absolute errors of the model to be between 0.0603 and 0.475 radians. We also explore the model's versatility using varying spot sizes to augment the transfer of this model across various systems where the focal length, aperture diameter, or light wavelength may differ, thereby influencing the number of pixels holding information across each irradiance pattern. Finally, this model is tested on experimentally collected data using a spatial light modulator, resulting in a mean absolute error of 0.909 radians. This research supports the development of a shock-wave-tolerant phase reconstruction algorithm for the Shack-Hartmann wavefront sensor. At large, robust shock-wave-tolerant phase reconstruction algorithms will improve wavefront sensing efforts where shock waves are present.

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Experimental investigation into the effects of shock waves on Shack-Hartmann wavefront sensor measurements

Cited by 3 publications

References 25 publications

Aero-optical effects, part II. Sources of aberrations: tutorial

Aero-optical effects, part II. Sources of aberrations: tutorial

Parametric studies of effects of finite pixel and AOI sizes on the accuracy of Shack-Hartmann WFS

Phase discontinuity classification from single-aperture irradiance patterns using machine learning

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