Upset Dynamics of an Airliner Model: A Nonlinear Bifurcation Analysis

Gill, Stephen J.; Lowenberg, Mark H; Neild, Simon A; Krauskopf, Bernd; Puyou, Guilhem; Coetzee, Etienne

doi:10.2514/1.c032221

Cited by 41 publications

(33 citation statements)

References 38 publications

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“…This is because GTM was designed to investigate stall and post-stall regimes in the extended flight envelope regions. However, based on the results of the bifurcation analysis of GTM in [3], from  = 10.5, the aircraft enters an undesirable regime with steep helical spirals which are considered as upset conditions and must be recovered from, by stall recovery procedures. Hence, in the utilized database, the upper limit of  was constrained to 10.5.…”

Section: A Maneuvering Flight Envelopementioning

confidence: 99%

Investigating Impaired Aircraft's Flight Envelope Variation Predictability Using Least-Squares Regression Analysis

Norouzi

Kosari

Sabour

2020

Journal of Aerospace Information Systems

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Aircraft failures alter the aircraft dynamics and cause maneuvering flight envelope to change. Such envelope variations are nonlinear and generally unpredictable by the pilot as they are governed by the aircraft complex dynamics. Hence, in order to prevent in-flight Loss of Control it is crucial to practically predict the impaired aircraft's flight envelope variation due to any a-priori unknown failure degree. This paper investigates the predictability of the number of trim points within the maneuvering flight envelope and its centroid using both linear and nonlinear least-squares estimation methods. To do so, various polynomial models and nonlinear models based on hyperbolic tangent function are developed and compared which incorporate the influencing factors on the envelope variations as the inputs and estimate the centroid and the number of trim points of the maneuvering flight envelope at any intended failure degree. Results indicate that both the polynomial and hyperbolic tangent function-based models are capable of predicting the impaired fight envelope variation with good precision. Furthermore, it is shown that the regression equation of the best polynomial fit enables direct assessment of the impaired aircraft's flight envelope contraction and displacement sensitivity to the specific parameters characterizing aircraft failure and flight condition.Nomenclature V = total airspeed, knot ,  = angle of attack, sideslip angle, respectively, deg p, q, r = angular velocity components (roll rate, pitch rate, yaw rate, respectively), deg/s 1 Ph.D. Candidate, Faculty of New Sciences and Technologies; ramin.norouzi@ut.ac.ir. Student Member AIAA. 2 Associate Professor, Faculty of New Sciences and Technologies; kosari_a@ut.ac.ir. 3 Assistant Professor, Faculty of New Sciences and Technologies; sabourmh@ut.ac.ir. , , ,  = Euler angles (roll, pitch, yaw, respectively), flight path angle, deg , = state vector, control vector, respectively , , , ℎ = deflection angles (elevator, aileron, rudder, respectively), deg, throttle setting (%) ∈ [0, 1] , �, = fitted model's residual, outcome, parameters (coefficients) vectors, respectively , = predictor vector, model parameters (coefficients) vector, respectively = total degree of polynomial , ℍ, , = gradient, Hessian, Jacobian of the objective function , identity matrix, respectively = Newton's method search direction = Marquardt parameter = hyperbolic tangent function 1 , 2 = total number of neurons in the first (hidden) layer, second (last) layer, respectively = backpropagation training iteration ℒ , ℒ , ℒ , ℒ, = (net output, net input, bias) of ℒ ℎ neuron in layer , respectively ℒ,= weight's element of ℒ ℎ neuron in layer due to ℎ neuron of the previous layer .̂ = standard backpropagation sensitivity, Marquardt sensitivity, respectively ℰ, = contribution of the ℰ ℎ neuron of the last (second) layer to the error of the ℎ training sample = statistical expected value of the ℎ input factor Subscripts , = total number of training samples, total number of model parameters, res...

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Section: A Maneuvering Flight Envelopementioning

confidence: 99%

Investigating Impaired Aircraft's Flight Envelope Variation Predictability Using Least-Squares Regression Analysis

Norouzi

Kosari

Sabour

2020

Journal of Aerospace Information Systems

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“…Due to its rich and freely available data, NASA's generic transport model (GTM) is well-recognized in aerospace engineering community and widely used in literature [6,7,[14][15][16][19][20][21][22][23]. Representing a 5.5 % down-scaled, typical aerial transport vehicle, the GTM provides an unmanned aerial vehicle [24] with exhaustive, full-envelope aerodynamic data from wind-tunnel studies [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Full-envelope, six-degrees-of-freedom trim analysis of unmanned aerial systems based on piece-wise polynomial aerodynamic coefficients

Cunis¹,

Condomines²,

Burlion³

2017

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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“…On the other hand, the use of numerical bifurcation analysis, implemented via continuation algorithms [9], has been shown to be extremely useful in developing an understanding of the characteristics of both stable and unstable solution branches in state-parameter space. This allows response types to be inferred not only locally but also 'globally' across a wide operating region [10]. Bifurcation analysis has, since Carroll and…”

mentioning

confidence: 99%

Impact of Controller Delays on the Nonlinear Dynamics of Remotely Piloted Aircraft

Gill

Lowenberg

Neild

et al. 2016

Journal of Guidance, Control, and Dynamics

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Time delays arise in most feedback systems and have specific relevance for remotely piloted vehicles with ground-based pilots and controllers. NASA's test facility for flight dynamics and control research using sub-scale vehicles, AirSTAR, has developed the remotely piloted Generic Transport Model (GTM). Analysis of a numerical model of this has previously provided insight into open-loop upset dynamics and the impact of flight controllers. However, to date, studies have not considered the effect of time delay on the system's stability. Current developments at the AirSTAR facility are aimed at testing a sub-scale generic airliner model during loss-of-control conditions over extended distances and altitudes compared to the GTM. In developing controllers for such a remotely piloted vehicle it is helpful to understand the effect of delay in the communication links and protocols. This paper utilises bifurcation analysis to evaluate the effect of delay on the closed-loop stability for the GTM numerical model with a linear quadratic regulator controller with proportional and integral components (LQR-PI). The impact of time delays in both fixed-gain and gain-scheduled versions of the controller are presented in terms of the stability of nominal and off-nominal solutions. This is followed by a discussion of stability maps, again generated by bifurcation analysis, which can be used to assess, over a wide

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Upset Dynamics of an Airliner Model: A Nonlinear Bifurcation Analysis

Cited by 41 publications

References 38 publications

Investigating Impaired Aircraft's Flight Envelope Variation Predictability Using Least-Squares Regression Analysis

Investigating Impaired Aircraft's Flight Envelope Variation Predictability Using Least-Squares Regression Analysis

Full-envelope, six-degrees-of-freedom trim analysis of unmanned aerial systems based on piece-wise polynomial aerodynamic coefficients

Impact of Controller Delays on the Nonlinear Dynamics of Remotely Piloted Aircraft

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