Atmospheric Turbulence Estimates from a Pulsed Lidar

Pruis, Matthew J.; Delisi, Donald P.; Ahmad, Nashat N.; Proctor, Fred H.

doi:10.2514/6.2013-512

Cited by 8 publications

(8 citation statements)

References 16 publications

(19 reference statements)

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“…from ultrasonic anemometers which were deployed at two heights on a tower. Estimates of EDR for each anemometer were obtained using a −5/3 power law which was fit to the portion of the spectrum that was contained within the inertial sub-range 4 . The EDR estimates at the two heights on the tower were extrapolated to the altitudes of the vortices using a similarity profile generator 5 .…”

Section: Atmospheric Effectsmentioning

confidence: 99%

Estimates of the Initial Vortex Separation Distance, b<sub>o</sub>, of Commercial Aircraft from Pulsed Lidar Data

Delisi

Pruis

Wang

et al. 2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Section: Atmospheric Effectsmentioning

confidence: 99%

Estimates of the Initial Vortex Separation Distance, b<sub>o</sub>, of Commercial Aircraft from Pulsed Lidar Data

Delisi

Pruis

Wang

et al. 2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

Self Cite

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“…The energy dissipation rate ε is obtained from the spectra using a power-law fit in the wave number region of the inertial subrange and dividing the fitted coefficient by the universal Kolmogorov constant C T 0.5 for a one-dimensional turbulence spectrum [46]. The order of magnitude of these values for the different conditions is in good agreement with the estimates obtained by Pruis et al [47] from pulsed measurements with a light detection and ranging system. The Kolmogorov length scale is computed with the kinematic viscosity, which is obtained from measurements of the flow temperature and the static pressure by the application of the ideal gas law and the Sutherland formula [48].…”

Section: A On-flow Characterizationmentioning

confidence: 80%

In-Flight Investigation of Transition Under Turbulent Conditions on a Laminar Wing Glove

2014

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Flight experiments carried out with a motorized glider yield insights into the aerodynamic processes acting on a laminar wing section under varying on-coming flow conditions. Measurement data are obtained on a laminar wing glove equipped with pressure transducers and wall microphones to detect transition and investigate the boundarylayer instabilities. The on-coming flow, including the instantaneous angle of attack and the turbulence characteristics, is measured with hot-wire sensors. The flight tests were carried out under calm and moderately turbulent conditions. Characteristic results for both on-coming flow conditions are presented and differences are analyzed. Selected comparisons of the experimental results with steady boundary-layer and linear stability computations are provided. The effects observed in the experiments cannot be described solely by quasi-steady theory. Unsteady changes of the pressure distribution are identified as one source for the modification of the transition development. A conclusion on the observed superimposed effect of increased levels of atmospheric small-scale turbulence on boundary-layer receptivity cannot yet be drawn from the present experiments. Nomenclature= turbulent kinetic energy density spectrum, m∕s 2 ∕m −1 E xx = one-dimensional energy density spectrum, m∕s 2 ∕m −1 F = dimensionless stream function, -k = wave number, 1∕m k x = streamwise wave number, -k z = spanwise wave number, -L I = integral length scale, m L ref = reference length, m l k = Kolmogorov length scale, m m = dimensionless pressure gradient, -N = N factor, -P = pressure, Pa P = mean pressure, Pa p 0 = pressure fluctuation, Pa q 0 = dimensionless disturbance vector, -q = dimensionless disturbance amplitude function, -Re = Reynolds number, -Tu xy = two-component turbulence intensity, -U = velocity vector U; V; W T , m∕s U = mean velocity vector U; V; W T , m∕s U e = edge velocity, m∕s U eff = effective velocity, m∕s U ref = reference velocity, m∕s U TAS = true airspeed (U ∞ ), m∕s U ∞ = freestream velocity, m∕s u 0 = fluctuation velocity vector u 0 ; v 0 ; w 0 T , m∕s x = streamwise coordinate, m y = wall-normal coordinate, m z = spanwise coordinate, m α = angle of attack, deg β = sideslip angle, deg ε = energy dissipation rate, m 2 ∕s 3 η = dimensionless Falkner-Skan variable, -θ = probe axis inclination to the flow, deg ν = kinematic viscosity, m 2 ∕s ρ = air density, kg∕m 3 Ψ = stream function, m 2 ∕s ψ = yaw angle, deg ψ = wire slant angle for θ equal to zero, deg ψ eff = effective yaw angle, deg ω = angular frequency, 1∕s

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“…As noted in Delisi et al [2013], headwinds affect apparent descent rates by advecting vortices generated at an earlier (tailwind) or later time (headwind) into the plane of the lidar observations. When observing vortices near the ground, this issue is additionally complicated by interaction of the vortices with the ground.…”

Section: A Headwinds Affect Apparent Trajectories and Circulation Ofmentioning

confidence: 97%

Fast-time Wake Vortex Model Predictions Compared with Observations Behind Landing Aircraft Near the Ground (Invited)

Pruis

Delisi

Jacob

et al. 2015

7th AIAA Atmospheric and Space Environments Conference

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NASA has recently collected a large dataset of wake vortex and weather observations at Memphis International Airport. The wake data consists primarily of vortices generated between one and two wingspans above the ground. Most of the landings in the dataset include field observations of all the required environmental and aircraft data needed for input into fast-time wake vortex prediction models; including vertical profiles of wind speed, air temperature and eddy dissipation rate, as well as the aircraft weights. Prior to the collection of this data set there was only a small amount of data that could be used in the evaluation of the skill of fast-time wake vortex prediction models when the vortices were generated within a couple wingspans of the ground surface. This new data set significantly increases the amount of data available for the assessment of the models in this important region. Comparison of three fast-time model predictions with the new Memphis data indicates there are several areas where the models can be improved in the in ground effect region; specifically better parameterization of the effect of turbulence, and inclusion of the effect of headwind. Nomenclature APA = AVOSS Prediction Algorithm (a fast-time wake vortex model) AVOSS = Aircraft VOrtex Spacing System b = vortex separation distance, m b o = initial vortex separation distance, m EDR, ε = eddy dissipation rate, m 2 /s 3 EDR* = nondimensional eddy dissipation rate, = (ε b o ) 1/3 / v o g = acceleration due to gravity, m 2 /s IGE = In-Ground Effect region HW = Headwind, m/s LMCT = Lockheed Martin Coherent Technologies NGE = Near-Ground Effect region OGE = Out-of-Ground Effect region RoI = Region of Interest, used by Lockheed Martin Offline Wake Processing algorithm SNR = Signal to Noise Ratio, dB AIAA Aviation 2 T = time, seconds T* = nondimensional time, where T * = T / (b o / v o ) TDAWP = TASS Driven Algorithms for Wake Prediction (a fast-time wake vortex model) u = crosswind velocity, m/s U a = airspeed, m/s v o = initial vortex descent rate, m/s W = aircraft weight, kg Y = vortex lateral position, m Z = vortex altitude, m Z* = nondimensional vortex height, = Z/b o Γ ο = initial vortex circulation intensity, = gW/(2πρU a b o ), m 2 /s Γ* = nondimensional circulation intensity, = Γ/Γ o ρ= air density, kg/m 3 ϴ = potential temperature, K φ = glide slope, degrees

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Atmospheric Turbulence Estimates from a Pulsed Lidar

Abstract: Estimates of the eddy dissipation rate (EDR

Cited by 8 publications

References 16 publications

Estimates of the Initial Vortex Separation Distance, b<sub>o</sub>, of Commercial Aircraft from Pulsed Lidar Data

Estimates of the Initial Vortex Separation Distance, b<sub>o</sub>, of Commercial Aircraft from Pulsed Lidar Data

In-Flight Investigation of Transition Under Turbulent Conditions on a Laminar Wing Glove

Fast-time Wake Vortex Model Predictions Compared with Observations Behind Landing Aircraft Near the Ground (Invited)

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