Holographic Wavefront Sensing for Atmospheric Turbulence using Karhunen-Loève Decomposition

2016 ◽  
Author(s):  
Esdras Anzuola ◽  
Andreas Zepp ◽  
Pablo Marin ◽  
Szymon Gladysz ◽  
Karin Stein
Keyword(s):  
Atmospheric Turbulence ◽  
Wavefront Sensing

scholarly journals Deep learning wavefront sensing and aberration correction in atmospheric turbulence

PhotoniX ◽  
2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Kaiqiang Wang ◽  
MengMeng Zhang ◽  
Ju Tang ◽  
Lingke Wang ◽  
Liusen Hu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Atmospheric Turbulence ◽  
Wavefront Sensor ◽  
Wavefront Aberration ◽  
Wavefront Sensing ◽  
Aberration Correction ◽  
Single Object ◽  
Multiple Objects ◽  
Correction Effect ◽  
And Performance

AbstractDeep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor (i.e. reconstruction of the wavefront aberration phase from the distorted image of the object). We compared and found the characteristics of the direct and indirect reconstruction ways: (i) directly reconstructing the aberration phase; (ii) reconstructing the Zernike coefficients and then calculating the aberration phase. We verified the generalization ability and performance of the network for a single object and multiple objects. What’s more, we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

Estimating Atmospheric Turbulence Parameters from the Envelopes of Radio Acoustic Signals

2001 ◽  
Vol 55 (8) ◽  
pp. 5
Author(s):  
V. M. Kartashov ◽  
V. A. Petrov ◽  
Ye. G. Proshkin ◽  
G. I. Sidorov
Keyword(s):  
Atmospheric Turbulence ◽  
Acoustic Signals ◽  
Turbulence Parameters

Numerical study of wake vortex decay and descent in homogeneous atmospheric turbulence

AIAA Journal ◽  
2000 ◽  
Vol 38 ◽  
pp. 643-656 ◽  
Author(s):  
Jongil Han ◽  
Yuh-Lang Lin ◽  
S. P. Arya ◽  
Fred H. Proctor
Keyword(s):  
Atmospheric Turbulence ◽  
Numerical Study ◽  
Wake Vortex

A striped pattern of snowfall and snow cover

1993 ◽  
Vol 18 ◽  
pp. 27-32
Author(s):  
Yasuaki Nohguchi ◽  
Takashi Ikarashi ◽  
Osamu Abe ◽  
Atsushi Sato
Keyword(s):  
Snow Cover ◽  
Atmospheric Turbulence ◽  
Layered Structure ◽  
Time Variation ◽  
Vertical Wall ◽  
Striped Pattern

A striped pattern can be seen by spraying ink on a vertical wall of a snow pit to observe the layered structure of a snow cover. This pattern is caused by variations of snowfall in time, particularly pauses in snowfall, and its structure is related to a kind of fractal. In this paper, we consider snowfall and snow cover from a viewpoint of fractals and show that the layered structure of snow cover is a record of fractals on atmospheric-turbulence phenomena through the time variation of snowfall.

Quasi-Wavelet Models for Atmospheric Turbulence

2002 ◽  
Author(s):  
George H. Goedecke ◽  
D. K. Wilson ◽  
Vladimir E. Ostashev ◽  
Harry J. Auvermann
Keyword(s):  
Atmospheric Turbulence
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