Investigation of pitch damping derivatives for the Standard Dynamic Model at high angles of attack using neural network

Tatar, Massoud; Masdari, Mehran

doi:10.1016/j.ast.2019.06.046

Cited by 18 publications

(5 citation statements)

References 19 publications

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“…The high α LCO region is more The nature of this type of rig, where the aircraft model motion is driven by its own control surfaces, is seen to be particularly well suited to studies of complex or counter-intuitive behaviours such as in the initiation of aircraft upset/loss-of-control scenarios. Future application of this technique could be used to enhance: flight characteristics modelling, to develop and evaluate online system identification techniques [40], to extract stability derivatives in combination with Machine Learning methods [43] or to validate CFD simulations of novel aircraft in subsonic regimes [6]; and for design and evaluation of flight control laws, like Machine Learning-based attitude controllers for fixed-wing UAVs [4].…”

Section: Discussionmentioning

confidence: 99%

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Capturing nonlinear time-dependent aircraft dynamics using a wind tunnel manoeuvre rig

Araujo-Estrada

Lowenberg

Neild

2022

Aerospace Science and Technology

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Section: Discussionmentioning

confidence: 99%

“…Note that forced-oscillation experiments can be conducted using the model control surfaces to acquire dynamic stability derivatives [6,43], as well as unsteady aerodynamic characteristics [17].…”

Section: Experimental Platformmentioning

confidence: 99%

Capturing nonlinear time-dependent aircraft dynamics using a wind tunnel manoeuvre rig

Araujo-Estrada

Lowenberg

Neild

2022

Aerospace Science and Technology

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“…The utilization of neural networks (NN) to understand the evolution of nonlinear equations has been demonstrated before [35], regardless of uncertainty. Scientifically, this infers NN will learn flight mechanics equations [36] and produce an equal outcome.…”

Section: Neural Network Based Gravity Vector Estimationmentioning

confidence: 99%

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

2020

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The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems’ development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant; it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment.

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“…One of the early SI techniques applied for detecting damage is linear dynamic parameters extraction, also referred to as modal analysis [25]. This technique provides insights into dynamic features such as vibration modes [26], internal resonances [27], damping [28], and structural stiffness [21]. Modal analysis can be employed to relate fatigue life to the dynamic parameters of structures subjected to shock [29], harmonic [30], rotational [24], or random excitation [31].…”

Section: Introductionmentioning

confidence: 99%

Highly Sensitive Nonlinear Identification to Track Early Fatigue Signs in Flexible Structures

Habtour

Maio

Masmeijer

et al. 2021

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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This study describes a physics-based and data-driven nonlinear system identification approach for detecting early fatigue damage due to vibratory loads. The approach also allows for tracking the evolution of damage in real-time. Nonlinear parameters such as geometric stiffness, cubic damping and phase angle shift can be estimated as a function of fatigue cycles, which are demonstrated experimentally using flexible aluminum 7075-T6 structures exposed to vibration. Nonlinear system identification is utilized to create and update nonlinear frequency response functions, backbone curves and phase traces to visualize and estimate the structural health. Findings show that the dynamic phase is more sensitive to the evolution of early fatigue damage than nonlinear parameters such as the geometric stiffness and cubic damping parameters. A modifed Carrella-Ewins method is introduced to calculate the backbone from the nonlinear signal response, which is in good agreement with the numerical and harmonic balance results. The phase tracing method is presented, which appears to detect damage after approximately 40% of fatigue life, while the geometric stiffness and cubic damping parameters are capable of detecting fatigue damage after approximately 50% of the life-cycle.

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Investigation of pitch damping derivatives for the Standard Dynamic Model at high angles of attack using neural network

Cited by 18 publications

References 19 publications

Capturing nonlinear time-dependent aircraft dynamics using a wind tunnel manoeuvre rig

Capturing nonlinear time-dependent aircraft dynamics using a wind tunnel manoeuvre rig

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

Highly Sensitive Nonlinear Identification to Track Early Fatigue Signs in Flexible Structures

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