A spatial model of wind shear and turbulence

With the growth of civil aviation traffic capacity, safety and environmental considerations urge today for the development of guidance systems with improved accuracy for spatial and temporal trajectory tracking. This should induce increased capacity by allowing safe operations at minimum separation standards. Also, at take-off and landing, trajectory dispersion should be reduced resulting in controlled noise impacts on airport surrounding communities. Current civil aviation guidance systems operate with real time corrective actions to maintain the aircraft trajectory as close as possible to the planned trajectory. In this paper, we consider the problems of designing new longitudinal guidance control laws for an autopilot so that accurate vertical tracking and overfly time are better insured. Instead of using time as the independent variable to describe the guidance dynamics of the aircraft, we adopt distance to land, which can be considered today to be available online with acceptable accuracy and availability. A new representation of aircraft longitudinal guidance dynamics is developed according to this spatial variable. Then a nonlinear inverse control law based-on this new proposed spatial representation of guidance dynamics is established to make the aircraft follow accurately a vertical profile and a desired airspeed. The desired airspeed is then regulated to make the aircraft overfly different waypoints according to a planned timetable. Then simulations experiments with different wind conditions are performed for a transportation aircraft performing a general descent approach for landing. These simulation results are compared with those obtained from a classical time-based guidance control law.

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“…is invertible [18], it is possible to perform the dynamic inversion to get effective trajectory tracking control laws, [22]. So we get:…”

Section: Space-based Nli Tracking Controlmentioning

confidence: 99%

Aircraft time-2d longitudinal guidance based on spatial inversion of flight dynamics

Bouadi

Choukroun

Mora‐Camino

2012

2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC)

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“…In the MATLAB/Simulink implementation of the Dryden turbulence model, Monte Carlo simulation [43] is used, i.e. Gaussian white noise, with unity spectral density, is input to a linear filter.…”

Section: Dryden Turbulence Modelmentioning

confidence: 99%

Flight Data Analysis and Simulation of Wind Effects During Aerial Refueling

Lewis

2008

Journal of Aircraft

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To my brothers Ben and John, from whom I have learned much. ACKNOWLEDGEMENTSI am continually grateful to my supervising professor, Dr. Atilla Dogan, for his support of my graduate work at UTA. I have greatly benefited from the opportunities for research and publishing he has provided over the last two years, and I thank him for involving me early in research during my first semester in the master's program.His knowledge and guidance have proven invaluable to my academic career.

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“…In this microburst model with turbulence, we assume that turbulence and microburst windshear can be modeled independently. Experimental evidence suggests that turbulence length scales increase through a microburst and, in some unknown manner, depend on microburst size and strength [17]. Such a functional dependence between turbulence and microburst parameters will result in a nonstationary and non-Gaussian wind process description [9].…”

Section: = Cmcand + + C Mq -Q • (15)mentioning

confidence: 99%

Modified guidance laws for escaping a microburst with turbulence

Dogan¹,

Kabamba²

2000

18th Applied Aerodynamics Conference

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This paper introduces Modified Altitude-and Dive-Guidance laws for escaping a microburst with turbulence. First, a new safety metric is constructed that quantifies the aircraft upward force capability in a microburst encounter. In the absence of turbulence, the safety factor is shown to be a decreasing function of altitude. This suggests that descending to a low altitude may improve safety in the sense that the aircraft will have more upward force capability to maintain its altitude. In the presence of stochastic turbulence, the safety factor is treated as a random variable and its probability distribution function is analytically approximated as a function of altitude. This approximation allows us to determine the highest safe altitude at which the aircraft may descend, hence avoiding to descend too low. This highest safe altitude is used as the commanded altitude in Modified Altitude-and Dive-Guidance. Monte Carlo simulations show that these Modified Altitude-and Dive-Guidance strategies can decrease the probability of minimum altitude being less than a given value without compromising the probability of crash. That is, an aircraft with Modified Altitude-or Dive-Guidance can have a higher recovery altitude without increasing the risk of ground contact or stall. 1

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A spatial model of wind shear and turbulence

Cited by 9 publications

References 3 publications

Aircraft time-2d longitudinal guidance based on spatial inversion of flight dynamics

Aircraft time-2d longitudinal guidance based on spatial inversion of flight dynamics

Flight Data Analysis and Simulation of Wind Effects During Aerial Refueling

Modified guidance laws for escaping a microburst with turbulence

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