Research into reduction of Loss-of-Control accident rate in a meaningful and measurable way through the use of innovative pilot training techniques

Priest, James E.

doi:10.2514/6.2014-1005

Cited by 4 publications

(2 citation statements)

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“…The Colgan Air 3407 and Air France Flight 447 accidents 1,2 that occurred in February and June 2009, respectively, highlighted deficiencies in pilot training with respect to recovering the aircraft from stall and upset states. Furthermore, a recently-concluded 16 year study by Calspan, 3 funded mainly by NASA, the Federal Aviation Administration (FAA), and the United States Navy, concluded that pilots were ill-equipped and untrained to recognize an aircraft in an upset state and then perform any appropriate control inputs to successfully recover from this state.…”

Section: Introductionmentioning

confidence: 98%

Stall/Post-Stall Modeling of the Longitudinal Characteristics of a General Aviation Aircraft

Ananda

Selig

2016

AIAA Atmospheric Flight Mechanics Conference

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The high rate of accidents in general aviation due to pilot loss of control has necessitated the need to find better methods of training pilots. Pilot control and awareness in the stall/post-stall regime can be improved through the use of higher fidelity flight simulators. To create a high fidelity aerodynamic model in the stall/post-stall regime to be implemented in a flight simulator, both steady and unsteady aircraft characteristics need to be represented. In this paper, the detailed development of a steady-state general aviation aircraft stall/post-stall longitudinal aerodynamic model is described. The steady-state model uses a component buildup approach that uses strip theory to output the aerodynamic forces and moments of an aircraft. An integrated non-linear lifting-line theory approach is used to model the wing and horizontal tail. Longitudinal effects due to elevator deflections are also included. Validation studies are performed against static wind tunnel datasets for a typical single-engine low-wing general aviation aircraft design. Nomenclatureairfoil moment coefficient at quarter chord C M c/4 = surface moment coefficient at quarter chord (= M/ 1 2 ρV 2 ∞ S ref c) C N = surface normal force coefficient (= N/ 1 2 ρV 2 ∞ S ref ) C n = local normal-force coefficient per unit length * Graduate Student (Ph.D. Candidate), 104 S. Wright St., AIAA Student Member. anandak1@illinois.edu † Professor, 104 S. Wright St., AIAA Associate Fellow. m-selig@illinois.edu 1 of 25 American Institute of Aeronautics and Astronautics Downloaded by PURDUE UNIVERSITY on June 22, 2016 | http://arc.aiaa.org | AIAA Aviation d F = fuselage cross-section diameter D = drag D ind = induced drag k = corner radius l ref = reference length l F = fuselage length L = lift M = moment n P = number of lifting surface panels N = normal force r F = fuselage cross-section radius Re = Reynolds number based on mean aerodynamic chord (= V ∞ c/ν) S = lifting surface area S f = flap area S ref = reference area V ∞ = freestream velocity V F = fuselage volume w ind = velocity induced by shed vortices V rel = relative velocity w F = fuselage width x F = axial distance from fuselage nose x ac F = distance from nose to aerodynamic center of fuselage x c F = distance from nose to centroid of fuselage planform area x m F = distance from nose to centroid of fuselage pitching-moment reference center x P S , y P S , z P S = location of spanwise panel station points x LLT , y LLT , z LLT = location of lifting-line points Y = side force α ef f = effective angle of attack α geo = geometric angle of attack α ind = induced angle of attack β = sideslip angle δ f = flap deflection angle η = flap effectiveness correction factor η f use = fuselage crossflow drag proportionality factor Γ = circulation Γ 0 = circulation at center-span Γ LLT = circulation at the lifting-line points Γ SV = shed vortex circulation τ = flap effectiveness factor θ f = factor relating flap to chord ratio to flap effectiveness factor ν = kinematic viscosity ρ = density of air Subscripts c/4 = ...

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Section: Introductionmentioning

confidence: 98%

Stall/Post-Stall Modeling of the Longitudinal Characteristics of a General Aviation Aircraft

Ananda

Selig

2016

AIAA Atmospheric Flight Mechanics Conference

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“…However, on the flip side this ''safety net'' have taken a toll on the pilot's efficacy in addressing such situations. 10 It is important to note that most of the autopilot systems are designed for nominal conditions and disengage most often without a warning when the situation goes haywire. The current protection schemes are designed based on known limits and their potency to act are under serious question under upset and vehicle impairment conditions.…”

Section: Introductionmentioning

confidence: 99%

Improved pilot training via bifurcation analysis and robust control for aircraft loss of control problems

Rohith

Sinha

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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Aircraft loss of control is one of the largest contributors to fatal accidents in the aviation environment. The unprecedented change in aircraft dynamics due to loss of control onset and the associated structural constraints make loss of control prevention and/or recovery a challenging task. State-of-the-art autopilots are generally designed for nominal aircraft operations and disengage under off-nominal conditions, hence cannot be viewed as a safety solution during loss of control onsets. Herein lies the importance of providing training to pilots so as to equip themselves to rescue aircraft from loss of control events. Current pilot training methodologies have significant limitations when it comes to loss of control prevention and recovery strategies. In this context, a simulator for improved pilot training based on bifurcation and continuation techniques is presented in the paper. Augmenting these techniques with the current pilot training procedure can significantly improve the quality of training. This methodology can help pilots to distinguish various loss of control scenarios and aid them in taking appropriate recovery decisions intuitively. Meanwhile, a robust control-based loss of control handling module is also presented for developing non-intuitive strategies for loss of control prevention and recovery. This module can simulate adequate control profiles that the pilot can follow to get in and out of various loss of control scenarios. Moreover, it can be used as pilot activated recovery system in case of pilot disorientation and as a fully autonomous recovery system for much complex scenarios. The simulator is developed in MATLAB/SIMULINK platform and is shown to realize diverse loss of control events like spiral, spin, etc., and subsequent recovery from the same.

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Aircraft Loss of Control Problem Analysis and Research Toward a Holistic Solution

Belcastro¹,

Foster²,

Shah³

et al. 2017

Journal of Guidance, Control, and Dynamics

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Research into reduction of Loss-of-Control accident rate in a meaningful and measurable way through the use of innovative pilot training techniques

Cited by 4 publications

References 0 publications

Stall/Post-Stall Modeling of the Longitudinal Characteristics of a General Aviation Aircraft

Stall/Post-Stall Modeling of the Longitudinal Characteristics of a General Aviation Aircraft

Improved pilot training via bifurcation analysis and robust control for aircraft loss of control problems

Aircraft Loss of Control Problem Analysis and Research Toward a Holistic Solution

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