Modeling the Radar Signature of Raindrops in Aircraft Wake Vortices

Abstract The present work is dedicated to the modeling and simulation of the radar signature of raindrops within wake vortices. This is achieved through the computation of the equation of raindrop motion within the wake vortex flow. Based on the inhomogeneous distribution of raindrops within wake vortices, the radar echo model is computed for raindrops in a given resolution cell. Simulated Doppler radar signatures of raindrops within wake vortices are shown to be a potential criterion for identifying wake vortex hazards in air traffic control. The dependence of the radar signature on various parameters, including the radial resolution and antenna elevation angle, is also analyzed.

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The Generation and Detection of Sound Emitted by Aircraft Wake Vortices in Ground Effect

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1718.1 ◽

2005 ◽

Vol 22 (5) ◽

pp. 543-554 ◽

Cited By ~ 2

Author(s):

William L. Rubin

Keyword(s):

Ground Effect ◽

Aircraft Noise ◽

Wake Vortex ◽

Wind Noise ◽

Vortex Sound ◽

Wake Vortices ◽

International Airport ◽

Aircraft Landing ◽

Aircraft Wake ◽

Noise Spectra

Abstract Sound recorded by the author in March 2002 at JFK International Airport shows that wake vortices in ground effect emit infrasound that is 1) more than 40 dB stronger than audible wake vortex sound; 2) substantially stronger than the infrasound component of wind noise and airport noise; and 3) comparable to, and often stronger than, the infrasound component of aircraft noise. Spectra and time plots of the magnitude of wake-vortex-generated sound are presented for aircraft landing on JFK runway 31R.

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Large eddy simulation of aircraft wake vortices within homogeneous turbulence - Crow instability

AIAA Journal ◽

10.2514/3.14409 ◽

2000 ◽

Vol 38 ◽

pp. 292-300 ◽

Cited By ~ 1

Author(s):

Jongil Han ◽

Yuh-Lang Lin ◽

David G. Schowalter ◽

S. P. Arya ◽

Fred H. Proctor

Keyword(s):

Large Eddy Simulation ◽

Homogeneous Turbulence ◽

Wake Vortices ◽

Eddy Simulation ◽

Large Eddy ◽

Aircraft Wake ◽

Crow Instability

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Study on the influence of linear and nonlinear distribution of crosswind on the motion of aircraft wake vortex

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410020987430 ◽

2021 ◽

pp. 095441002098743

Author(s):

Dong Li ◽

Ziming Xu ◽

Ke Zhang ◽

Zeyu Zhang ◽

Jinxin Zhou ◽

...

Keyword(s):

Numerical Simulations ◽

Dissipation Rate ◽

Vortex Pair ◽

Vertical Shear ◽

Wake Vortex ◽

Analysis Method ◽

Velocity Magnitude ◽

Aircraft Wake ◽

Linear And Nonlinear ◽

Nonlinear Distribution

Environmental crosswind can greatly affect the development of aircraft wake vortex pair. Previous numerical simulations and experiments have shown that the nonlinear vertical shear of the crosswind velocity can affect the dissipation rate of the aircraft wake vortex, causing each vortex of the vortex pair descent with different velocity magnitude, which will lead to the asymmetrical settlement and tilt of the wake vortex pair. Through numerical simulations, this article finds that uniform crosswind convection and linear vertical shear crosswind convection can also have an effect on the strength of the vortex. This effect is inversely proportional to the cube of the vortex spacing, so it is more intense on small separation vortex pair. In addition, the superposition of crosswind and vortex-induced velocities will lead to the asymmetrical pressure distribution around the vortex pair, which will also cause the tilt of the vortex pair. Furthermore, a new analysis method for wake vortex is proposed, which can be used to predict the vortex trajectory.

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Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Sensors ◽

10.3390/s21113937 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3937

Author(s):

Seungeon Song ◽

Bongseok Kim ◽

Sangdong Kim ◽

Jonghun Lee

Keyword(s):

Deep Learning ◽

Gesture Recognition ◽

Smart Home ◽

Doppler Radar ◽

Radar Images ◽

Radar Signature ◽

Memory Efficiency ◽

High Compression ◽

Value Decomposition ◽

Deep Learning Model

Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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