Interferometric velocity measurements through a fluctuating phase boundary using two Fresnel guide stars

Radner, Hannes; Büttner, Lars; Czarske, Jürgen

doi:10.1364/ol.40.003766

Cited by 19 publications

(5 citation statements)

References 18 publications

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“…Several attempts have been already undertaken in the past in order to reduce uncertainties in fluid flow measurements through an optical distortion [27] and even a fluctuating water surface [28]. In latter case a beam steering correction for a 0D measurement volume was accomplished.…”

Section: Resultsmentioning

confidence: 99%

Adaptive particle image velocimetry based on sharpness metrics

Teich

Grottke

Radner

et al. 2018

J. Eur. Opt. Soc.-Rapid Publ.

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Background: Optical distortions can significantly deteriorate the measurement accuracy in particle image velocimetry systems. Such distortions can occur at fluctuating phase boundaries during flow measurement and result from the accompanied refractive index changes. The usage of a wavefront sensor can be hindered by disturbing light reflexes or scattering. Methods: A combination of sharpness metric image evaluation and iterative optimization is demonstrated. The sharpness metric is used as an indicator for wavefront aberrations in order to correct low-order Zernike modes that influence the image quality of particle image velocimetry. Results: In this work we outline a sharpness metric based aberration correction with a deformable mirror, applied for the first time to particle image velocimetry. The proposed method allows for the reduction of systematic measurement uncertainties in particle image velocimetry. Conclusion: Our approach offers a new way to reduce static or slowly changing wavefront distortions in a fluid flow measurement setup in which a wavefront sensor is not applicable.

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Section: Resultsmentioning

confidence: 99%

Adaptive particle image velocimetry based on sharpness metrics

Teich

Grottke

Radner

et al. 2018

J. Eur. Opt. Soc.-Rapid Publ.

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“…In contrast to a guide star observed in transmission, the novel FGS technique [22]- [24] additionally implemented in the presented setup (see Fig. 1) uses the reflected light of the surface to measure the optical distortion induced by the interface.…”

Section: B Fresnel Guide Starmentioning

confidence: 99%

Field-Programmable System-on-Chip-Based Control System for Real-Time Distortion Correction in Optical Imaging

Radner

Stange

Büttner

et al. 2021

IEEE Trans. Ind. Electron.

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The digital transition requires real-time control of complex systems with short loop time and low latency in various applications. Field-programmable gate arrays (FP-GAs) are, in principle, capable of complying with this task but demand, on the other hand, a high programming effort. In this article, we propose a field-programmable system on chip (FPSoC) as a hybrid solution of an FPGA and a central processing unit (CPU) on a single monolithic die to combine the strengths of both architectures. An FPSoC-based adaptive optical wavefront correction system is presented as a case study to correct camera images in real time that are distorted by time-varying aberrations. While a short total loop time is achieved by interfacing the camera and a deformable mirror on a low level directly with the FPGA, all computationally nonintensive tasks are implemented on the CPU to keep the flexibility, reusability, and development expense low. The system corrects the optical distortion of water surface waves with up to 3600 control cycles per second and spatially attenuates the distortion up to Zernike polynomial 14 with up to 150 Hz. The FPSoC system enables fast spatiotemporal aberration correction in technical processes and offers a perspective for measuring complex flows through fluctuating interfaces.

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“…Estimating the fluid motion field through the distorted PIV images can only yield erroneous or distorted flow field measurements. In our previous work, a traditional adaptive optics system [18,19] and novel actuator-free adaptive optics technique [20] were used for correction distortion in a PIV imaging system. In [20], we established an adaptive optics technique without wavefront correction devices to correct PIV images, and then estimated the velocity distribution of the flow field using traditional Particle Image Velocimetry algorithms.…”

Section: Introductionmentioning

confidence: 99%

Flow Field Estimation with Distortion Correction Based on Multiple Input Deep Convolutional Neural Networks and Hartmann–Shack Wavefront Sensing

Gao,

Ge,

Zhu

et al. 2024

Photonics

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The precise estimation of fluid motion is critical across various fields, including aerodynamics, hydrodynamics, and industrial fluid mechanics. However, refraction at complex interfaces in the light path can cause image deterioration and lead to severe measurement errors if the aberration changes with time, e.g., at fluctuating air–water interfaces. This challenge is particularly pronounced in technical energy conversion processes such as bubble formation in electrolysis, droplet formation in fuel cells, or film flows. In this paper, a flow field estimation algorithm that can perform the aberration correction function is proposed, which integrates the flow field distribution estimation algorithm based on the Particle Image Velocimetry (PIV) technique and the novel actuator-free adaptive optics technique. Two different multi-input convolutional neural network (CNN) structures are established, with two frames of distorted PIV images and measured wavefront distortion information as inputs. The corrected flow field results are directly output, which are divided into two types based on different network structures: dense estimation and sparse estimation. Based on a series of models, a corresponding dataset synthesis model is established to generate training datasets. Finally, the algorithm performance is evaluated from different perspectives. Compared with traditional algorithms, the two proposed algorithms achieves reductions in the root mean square value of velocity residual error by 84% and 89%, respectively. By integrating both flow field measurement and novel adaptive optics technique into deep CNNs, this method lays a foundation for future research aimed at exploring more intricate distortion phenomena in flow field measurement.

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Interferometric velocity measurements through a fluctuating phase boundary using two Fresnel guide stars

Cited by 19 publications

References 18 publications

Adaptive particle image velocimetry based on sharpness metrics

Adaptive particle image velocimetry based on sharpness metrics

Field-Programmable System-on-Chip-Based Control System for Real-Time Distortion Correction in Optical Imaging

Flow Field Estimation with Distortion Correction Based on Multiple Input Deep Convolutional Neural Networks and Hartmann–Shack Wavefront Sensing

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