• Current Optics and Photonics
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• v.8 no.1
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• pp.38-44
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• 2024

Under the influence of atmospheric turbulence, a star's point image will shake back and forth erratically, and after exposure the originally small star point will spread into a huge spot, which will affect the ground calibration of the star sensor. To analyze the impact of atmospheric turbulence on the positioning accuracy of the star's center of mass, this paper simulates the atmospheric turbulence phase screen using a method based on a sparse spectrum. It is added to the static-star-simulation device to study the transmission characteristics of atmospheric turbulence in star-point simulation, and to analyze the changes in star points under different atmospheric refractive-index structural constants. The simulation results show that the structure function of the atmospheric turbulence phase screen simulated by the sparse spectral method has an average error of 6.8% compared to the theoretical value, while the classical Fourier-transform method can have an error of up to 23% at low frequencies. By including a simulation in which the phase screen would cause errors in the center-of-mass position of the star point, 100 consecutive images are selected and the average drift variance is obtained for each turbulence scenario; The stronger the turbulence, the larger the drift variance. This study can provide a basis for subsequent improvement of the ground-calibration accuracy of a star sensitizer, and for analyzing and evaluating the effect of atmospheric turbulence on the beam.

• Current Optics and Photonics
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• v.7 no.6
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• pp.655-664
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• 2023

Ghost imaging (GI) technology is developing rapidly, but there are inevitably some limitations such as the influence of atmospheric turbulence. In this paper, we study a ghost imaging system in atmospheric turbulence and use a gamma-gamma (GG) model to simulate the medium to strong range of turbulence distribution. With a compressed sensing (CS) algorithm and generative adversarial network (GAN), the image can be restored well. We analyze the performance of correlation imaging, the influence of atmospheric turbulence and the restoration algorithm's effects. The restored image's peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) increased to 21.9 dB and 0.67 dB, respectively. This proves that deep learning (DL) methods can restore a distorted image well, and it has specific significance for computational imaging in noisy and fuzzy environments.

• Korean Journal of Optics and Photonics
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• v.34 no.3
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• pp.99-105
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• 2023

The performance of astronomical telescopes can be negatively affected by atmospheric turbulence. To address this issue, techniques for atmospheric turbulence correction have been developed, requiring the simulation of atmospheric turbulence in the laboratory. The most practical way to simulate atmospheric turbulence is to use a phase plate. When measuring a phase plate that simulates strong turbulence, a Shack-Hartmann wave-front sensor is commonly used. However, the laser power decreases as it passes through the phase plate, potentially leading to a weak laser signal at the sensor. This paper investigates the need to control the laser power when measuring a phase plate that simulates strong atmospheric turbulence, and examines the effects of the laser power on the measured wavefront. For phase plates with relatively high Fried parameter r₀, the laser power causes a variation of over 10% in r₀. For phase plates with relatively low r₀, the laser power causes a variation of less than 5%, which means that the influence of the laser power is negligible for phase plates that simulate strong atmospheric turbulence. Based on the system described in this paper, a phase plate simulating strong atmospheric turbulence can be measured at a laser power of 5 mW or higher. Therefore, controlling the laser's output power is necessary when measuring a phase plate for simulating atmospheric turbulence, especially for phase plates with low r₀ values.

• Wind and Structures
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• v.12 no.6
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• pp.553-574
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• 2009

This paper presents statistical analysis results of wind speed and atmospheric turbulence data measured from more than 30 anemometers installed at 15 different height levels on 325 m high Beijing Meteorological Tower and is primarily intended to provide useful information on boundary layer wind characteristics for wind-resistant design of tall buildings and high-rise structures. Profiles of mean wind speed are presented based on the field measurements and are compared with empirical models' predictions. Relevant parameters of atmospheric boundary layer at urban terrain are determined from the measured wind speed profiles. Furthermore, wind velocity data in longitudinal, lateral and vertical directions, which were recorded from an ultrasonic anemometer during windstorms, are analyzed and discussed. Atmospheric turbulence information such as turbulence intensity, gust factor, turbulence integral length scale and power spectral densities of the three-dimensional fluctuating wind velocity are presented and used to evaluate the adequacy of existing theoretical and empirical models. The objective of this study is to investigate the profiles of mean wind speed and atmospheric turbulence characteristics over a typical urban area.

• Atmosphere
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• v.19 no.3
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• pp.269-287
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• 2009

CAT (clear-air turbulence) forecasting algorithm, the Graphical Turbulence Guidance (GTG) system developed at NCAR (national center for atmospheric research), is evaluated with available observations (e.g., pilot reports; PIREPs) reported in South Korea during the recent 5 years (2003-2008, excluding 2005). The GTG system includes several steps. First, 44 CAT indices are calculated in the domain of the Regional Data Assimilation and Prediction System (RDAPS) analysis data with 30 km horizontal grid spacing provided by KMA (Korean Meteorological Administration). Second, 10 indices that performed ten best forecasting scores are selected. Finally, 10 indices are combined by measuring the score based on the probability of detection, which is calculated using PIREPs exclusively of moderate-or-greater intensity. In order to investigate the best performance of the GTG system in Korea, various statistical examinations and sensitivity tests of the GTG system are performed by yearly and seasonally classified PIREPs. Performances of the GTG system based on yearly distributed PIREPs have annual variations because the compositions of indices are different from each year. Seasonal forecasting is generally better than yearly forecasting, because selected CAT indices in each season represent meteorological condition much more properly than applying the selected CAT indices to all seasons. Wintertime forecasting is the best among the four seasonal forecastings. This is likely due to that the GTG system consists of many CAT indices related to the jet stream, and turbulence associated with the jet stream can be activated mostly in wintertime under strong jet magnitude. On the other hand, summertime forecasting skill is much less than other seasons. Compared with current operational CAT prediction system (KITFA; Korean Integrated Turbulence Forecasting System), overall performance of the GTG system is better when CAT indices are selected seasonally.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.474-484
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• 2017

This paper studies the applicability of an efficient numerical model based on artificial neural networks (ANNs) to predict the dynamic responses of the wing structure of an airplane due to atmospheric turbulence in the time domain. The turbulence velocity is given in the form of a stationary Gaussian random process with the von Karman power spectral density. The wing structure is modeled by a classical beam considering bending and torsional deformations. An unsteady vortex-lattice method is applied to estimate the aerodynamic pressure distribution on the wing surface. Initially, the trim condition is obtained, then structural dynamic responses are computed. The numerical solution of the wing structure's responses to a random turbulence profile is used as a training data for the ANN. The current ANN is a three-layer network with the output fed back to the input layer through delays. The results from this study have validated the proposed low-cost ANN model for the predictions of dynamic responses of wing structures due to atmospheric turbulence. The accuracy of the predicted results by the ANN was discussed. The paper indicated that predictions for the bending moments are more accurate than those for the torsional moments of the wing structure.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.2
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• pp.141-150
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• 2000

In order to genralize the vertical dispersion of plume at long distances on mesoscale over complex terrain dispersion coefficients data have been obtained systematically according to lapsed time after release by using a composite turbulence water tank that simulates convective boundary layer. Dispersion experiments have been carried out for various combined conditions of thermal turbulence intensity mechanical turbulence intensity and plume release height at slightly to moderately unstable conditions. Results of tracer dispersion experiments conducted using water tank camera and image processing system have been converted into atmospheric dispersion data through the application of similarity law. The equation $\sigma$z/Zi=aX/(b+c X2)0.5 where $\sigma$2; vertical dispersion coefficient zi : mixing height X : dimen-sionaless downwind distance was confirmed to be an appropriate and general equation for expressing $\sigma$2 variation with turbulence intensity and plume release height, The value of "a" was found to be principally affected by mechanical turbulence intensity and that of "b" by mechanical turbulence intensity and release height. It was confirmed that the magnitude of "c" varies with release height. Results of water tank experiments on the relationship of $\sigma$2 vs downwind distance x have been compared with actual atmospheric dispersion data such as CONDORS data and Bowne's nomogram Operating conditions of a composite turbulence water tank for simulating the field turbulence situations of CONDORS experiments and Bowne's $\sigma$2(x) nomogram for suburban area have also been investigated in terms of water temperature difference between convection water tank and bottom plate heating tank grid plate stroke mixing water depth length scale and velocity scale. Moreover the effect of mechanical turbulence intensity on vertical dispersion has been discussed in the light of release height and downwind distance. height and downwind distance.

• Atmosphere
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• v.25 no.1
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• pp.129-140
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• 2015

The statistical analysis of aviation turbulence occurred over South Korea and East Asia regions is performed, using pilot reports (PIREPs) during December 2002~November 2012 that were provided by the Korea Aviation Meteorological Agency (KAMA) and the National Center for Atmospheric Research (NCAR). In South Korea, the light (LGT)- and moderate or greater (MOG)-level turbulence events occurred most frequently in spring and winter due to strong vertical wind shear below or above jet stream in these seasons. Spatially, the LGT- and MOG-level events occurred mainly along domestic flight routes. The higher occurrences of the LGT- and MOG-level convectively induced turbulence (CIT) events show in spring and summer when convective systems frequently affect the Korean peninsula. The results are generally similar to a previous study on the aviation turbulence over South Korea during 2003~2008, except that MOG-level CIT events occurred more in February, June, and October. Over East Asia region, the LGT- and MOG-level events appeared mostly in summer and spring, respectively, and the highest occurrence is over the southeast region of Japan and Kamchatka peninsula near Russia.

• Current Optics and Photonics
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• v.6 no.5
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• pp.445-452
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• 2022

We developed an adaptive optics test bench using an optical simulator and two rotating phase plates that mimicked the atmospheric turbulence at Bohyunsan Observatory. The observatory was reported to have a Fried parameter with a mean value of 85 mm and standard deviation of 13 mm, often expressed as 85 ± 13 mm. First, we fabricated several phase plates to generate realistic atmospheric-like turbulence. Then, we selected a pair from among the fabricated phase plates to emulate the atmospheric turbulence at the site. The result was 83 ± 11 mm. To address dynamic behavior, we emulated the atmospheric disturbance produced by a wind flow of 8.3 m/s by controlling the rotational speed of the phase plates. Finally, we investigated how closely the atmospheric disturbance simulation emulated reality with an investigation of the measurements on the optical table. The verification confirmed that the simulator showed a Fried parameter of 87 ± 15 mm as designed, but a little slower wind velocity (7.5 ± 2.5 m/s) than expected. This was because of the nonlinear motion of the phase plates. In conclusion, we successfully mimicked the atmospheric disturbance of Bohyunsan Observatory with an error of less than 10% in terms of Fried parameter and wind velocity.

• Wind and Structures
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• v.19 no.6
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• pp.687-708
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• 2014

The performance of the $k-{\varepsilon}$ and $k-{\omega}$ two-equation turbulence models was investigated in computational simulations of the neutrally stratified atmospheric boundary layer developing above various terrain types. This was achieved by using a proposed methodology that mimics the experimental setup in the boundary layer wind tunnel and accounts for a decrease in turbulence parameters with height, as observed in the atmosphere. An important feature of this approach is pressure regulation along the computational domain that is additionally supported by the nearly constant turbulent kinetic energy to Reynolds shear stress ratio at all heights. In addition to the mean velocity and turbulent kinetic energy commonly simulated in previous relevant studies, this approach focuses on the appropriate prediction of Reynolds shear stress as well. The computational results agree very well with experimental results. In particular, the difference between the calculated and measured mean velocity, turbulent kinetic energy and Reynolds shear stress profiles is less than ${\pm}10%$ in most parts of the computational domain.