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Canary is the Multi-Object Adaptive Optics (MOAO) pathfinder for the future MOAO-assisted Integral-Field Units (IFU) proposed for Extremely Large Telescopes (ELT). The MOAO concept relies on tomographically reconstructing the turbulence…
Characterization of atmospheric turbulence is essential to understanding image quality of astronomical telescopes and applying adaptive optics systems. In this study, the vertical distributions of optical turbulence at the Peak Terskol…
Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is trying to achieve coordinated motion in turbulent wind…
There are different techniques to sense the wavefront phase-distortions due to atmospheric turbulence. Curvature sensors are practical in their sensitivity being adjustable to the prevailing atmospheric conditions. Even at the best sites,…
Accurate simulation of turbulent flow with separation is an important but challenging problem. In this paper, a data-driven Reynolds-averaged turbulence modeling approach, field inversion and machine learning is implemented to modify the…
Multiphase outflows, revealed by multi-wavelength observations, are crucial in redistributing gas and metals within and around galaxies. These outflows are often modelled theoretically using wind tunnel simulations of a cold ($\sim 10^4$ K)…
Serrations are commonly employed to mitigate the turbulent boundary layer trailing-edge noise. However, significant discrepancies persist between model predictions and experimental observations. In this paper, we show that this results from…
We propose a theoretical model for ice growth under a wind-driven supercooled water film. The thickness and surface velocity of the water layer are variable by changing the air stream velocity. For a given water supply rate, linear…
Winds launched at the scale of the accretion disc or dusty torus in Active Galactic Nuclei (AGN) are thought to drive energy-conserving outflows that shape galaxy evolution. The key signature of such outflows, the presence of a hot ($T…
Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate…
Forecast of optical turbulence and atmospheric parameters relevant for ground-based astronomy is becoming an important goal for telescope planning and AO instruments optimization in several major telescope. Such detailed and accurate…
Turbulence, namely, irregular fluctuations in space and time characterize fluid flows in general and atmospheric flows in particular.The irregular,i.e., nonlinear space-time fluctuations on all scales contribute to the unpredictable nature…
This work introduces the Area Localized Coupled (ALC) model, which extends earlier approaches to coupling classical wake superposition and atmospheric boundary layer models in order to enable applicability to arbitrary wind-farm layouts.…
This paper presents a novel feature fusion-based deep learning model (called CASU2Net) for fault detection in offshore wind turbines. The proposed CASU2Net model benefits of a two-step early fusion to enrich features in the final stage.…
Substellar companions such as exoplanets and brown dwarfs exhibit changes in brightness arising from top-of-atmosphere inhomogeneities, providing insights into their atmospheric structure and dynamics. This variability can be measured in…
Reynolds-Averaged Navier-Stokes(RANS) method will still play a vital role in the following several decade in aerospace engineering. Although RANS models are widely used, empiricism and large discrepancies between models reduce the…
Moving clouds affect the global solar irradiance that reaches the surface of the Earth. As a consequence, the amount of resources available to meet the energy demand in a smart grid powered using Photovoltaic (PV) systems depends on the…
The Shack-Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the…
Turbulence is a phenomena that is {\it locally} and statistically characterized by measurements, but it is caused by {\it nonlocal} energy cascades associated with the environment. The presence of turbulence coincides with fluctuations in…
This paper describes a novel deep learning-based method for mitigating the effects of atmospheric distortion. We have built an end-to-end supervised convolutional neural network (CNN) to reconstruct turbulence-corrupted video sequence. Our…