A spatial model of wind shear and turbulence

Campbell, C. Warren; Sanborn, V.

doi:10.2514/3.45065

Cited by 13 publications

(3 citation statements)

References 1 publication

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“…Shi et al [25] and Hussin et al [26] employed multiple probability distributions for wind speed data modeling to ascertain the goodness-of-fit. Other researchers [27][28][29] experimentally authenticated turbulence and probed its underlying mechanism. Given the heightened atmospheric instability caused by global warming, turbulence forecasting has emerged as a crucial method for ensuring aircraft safety.…”

Section: Related Workmentioning

confidence: 99%

Visualization model for low-level turbulence using temporal rate of change of headwind of an aircraft

Ito,

Ohba,

Mizuno

2023

Preprint

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In this study, we focused on the temporal rate of change of headwind, which is one of the recorded parameters in the aircraft’s in-flight quick access recorder data. We selected the Laplace distribution and utilized the scaling parameter to construct a low-level turbulence visualization model. Using this model, we calculated the risk of low-level turbulence occurrence at five airports in Japan based on the month, time of day, and wind speed. We visualized how the geographical conditions at each airport influenced risk in relation to airport wind speeds. We developed a low-level turbulence visualization site linked to weather conditions using these results to enable pilots to easily verify low-level turbulence risk and incorporate this information into their flight routines. These findings are anticipated to significantly enhance aircraft safety.

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Section: Related Workmentioning

confidence: 99%

Visualization model for low-level turbulence using temporal rate of change of headwind of an aircraft

Ito,

Ohba,

Mizuno

2023

Preprint

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“…The atmospheric turbulence was modeled by a three-dimensional spatially frozen field consisting of three velocity components. 7 This model has been further developed to generate turbulent gusts, which have all the correlations and cross correlations defined by the theory of isotropic turbulence.…”

Section: Turbulence Modelmentioning

confidence: 99%

“…The upper path in the diagram operates on the specific force f B . Specific force is defined as the difference between inertial acceleration a and the acceleration due to gravity g f = a~g (7) This signal is scaled and then transformed into the groundfixed inertial frame F/ by the transformation matrix L fs . The vector g/ is then added to create an inertial acceleration signal which is passed through the high-pass filters of block HP FILT 1.…”

Section: Simulator Motion Augmentationmentioning

confidence: 99%

Augmenting flight simulator motion response to turbulence

Reid

Robinson

1990

Journal of Aircraft

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When flight simulator motion systems are tuned for acceptable response to pilot control inputs, it is found that their response to turbulence may be unduly attenuated. The motion augmentation technique described in this paper allows the designer to increase the simulator's motion response to turbulence without otherwise altering its flight characteristics. The method employs a second set of flight equations excited only by the turbulence signals. The output from these equations is added to that from the primary simulation flight equations before being fed to the motion system. A sample case is developed showing its successful application to the heave channel of the University of Toronto Institute for Aerospace Studies Flight Research Simulator.cKO a 2 *(*) Nomenclature : heave (or z-axis) acceleration of the aircraft cockpit expressed as an F B component = simulator-fixed body axis frame = specific force at aircraft cockpit expressed in F B components = longitudinal turbulence correlation = specific force at simulator cockpit expressed in F s components = acceleration due to gravity expressed in Fj components = lateral turbulence correlation = A: + k + k • • wavenumber expressed in cycles per meter: [Ari k 2 k,] T • -the Fourier transform of n(x) • three independent Gaussian white noise sources:[n\ n 2 n 3 ] T • • the autocorrelation function for x(t) at time delay r • • V/f + rl + r\ • spatial separation between two points in F/: = spatial coordinates in F/ = vertical displacement of simulator in Fj = Kronecker delta = modulating variable = turbulence intensity = turbulence spectrum function [3x3 matrix with elements $//(£)] xx(^) = power spectral density of x(t) $ xy (o)) = cross-power spectral density between x(t) and y(t) co = frequency (rad/s)

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Generation of spatial atmospheric turbulence field in aircraft motion simulation based on refined similarity hypothesis

Guo

Chen

et al. 2010

Sci. China Technol. Sci.

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A spatial model of wind shear and turbulence

Cited by 13 publications

References 1 publication

Visualization model for low-level turbulence using temporal rate of change of headwind of an aircraft

Visualization model for low-level turbulence using temporal rate of change of headwind of an aircraft

Augmenting flight simulator motion response to turbulence

Generation of spatial atmospheric turbulence field in aircraft motion simulation based on refined similarity hypothesis

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