Mathematical model of one flexible transport category aircraft

Sousa, Marcelo Santiago de; Paglione, Pedro; Silva, Roberto Gil Annes da; Cardoso-Ribeiro, Flávio Luiz; Cunha, Sebastião Simões da

doi:10.1108/aeat-12-2013-0230

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…The element 01 did not present this variation, possibly because this element is attached to the fuselage. The element 02 had a different behaviour when compared with the elements 03, 04 and 05, what can be explained by the presence of the engine in this element [24].…”

Section: Resultsmentioning

confidence: 89%

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A Detailed Physical Explanation of an Aircraft Flutter Mechanism

Siqueira,

Sousa,

Cardoso-Ribeiro

et al. 2024

ACRI

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Each dynamic mode (aeroelastic) is made up of torsional and rotational movements. These two movements in each mode were dissociated and the phase, amplitude, damping and frequency of each of these movements were analyzed. The structural resistances of torsion and bending, as well as the bending movement itself, have a damping effect and torsion has a destabilizing effect on the oscillations (if the centre of pressure is ahead of the flexural axis). After a certain speed, bending becomes out of phase with the applied forces. At this point, the bending has an amplifying effect on the oscillations and only the structural stiffness dampens the movement. From the speed at which the bending movement is out of phase with the applied aerodynamic loads, the damping of the mode decreases with speed, until flutter occurs. The type of analysis presented here was only possible due to the dissociation of torsion and bending movements in each mode. This is a novelty of this article. And this dissociation was made possible due to the use of the strain-based formulation, also called here as methodology NFNS_s (Non Linear Flight Dynamics – Non Linear Structural Dynamics – strain based formulation). The use of this methodology for this type of analysis was another contribution. The article presents the proposal of a new way of analyzing the aeroelastic stability of aircraft.

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Section: Resultsmentioning

confidence: 89%

“…The aircraft model presented here is the same presented in Sousa et.al., [24], but with two differences: the wing flexural axis is located at 75% mean aerodynamic chord and the structural stiffnesses are six times lower. Fig.…”

Section: Airplane Modelmentioning

confidence: 98%

A Detailed Physical Explanation of an Aircraft Flutter Mechanism

Siqueira,

Sousa,

Cardoso-Ribeiro

et al. 2024

ACRI

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“…Nevertheless, these methods are only suitable for small deformations in the linear aeroelasticity range. Contrariwise, for aircraft having large-scale structural deformations, the coupled nonlinear methods are appropriate (Shearer and Cesnik, 2007; Sousa et al , 2017; Palacios et al , 2010). However, these methods result in models having a large number of parameters.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear aeroelastic modeling of aircraft using support vector machine method

Bagherzadeh

2020

AEAT

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Purpose This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes. Design/methodology/approach A system identification method based on the support vector machine (SVM) is developed and applied to the nonlinear dynamics of an aeroelastic aircraft. In the proposed non-parametric gray-box method, force and moment coefficients are estimated based on the state variables, flight conditions and control commands. Then, flight parameters are estimated using aircraft equations of motion. Nonlinear system identification is performed using the SVM network by minimizing errors between the calculated and estimated force and moment coefficients. To that end, a least squares algorithm is used as the training rule to optimize the generalization bound given for the regression. Findings The results confirm that the SVM is successful at the aircraft system identification. The precision of the SVM model is preserved when the models are excited by input commands different from the training ones. Also, the generalization of the SVM model is acceptable at non-trained flight conditions within the trained flight conditions. Considering the precision and generalization of the model, the results indicate that the SVM is more successful than the well-known methods such as artificial neural networks. Practical implications In this paper, both the simulated and real flight data of the F/A-18 aircraft are used to provide aeroelastic models for its lateral-directional dynamics. Originality/value This paper proposes a non-parametric system identification method for aeroelastic aircraft based on the SVM method for the first time. Up to the author’s best knowledge, the SVM is not used for the aircraft system identification or the aircraft parameter estimation until now.

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“…In contrast, in order to consider large structural deformations, one should consider the nonlinear flight dynamics as well as the nonlinear structural dynamics. 1,8 In this approach, dissimilar nonlinear structural models such as the displacement-based, the strain-based, and the intrinsic models have been employed. 9 The approach provides models containing a large number of parameters; thus, these models are not suitable for applications such as the flight simulation due to high computational time and cost.…”

Section: Introductionmentioning

confidence: 99%

Flight dynamics modeling of elastic aircraft using signal decomposition methods

Bagherzadeh

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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To improve the precision and accuracy of the flight dynamic models for elastic aircraft, this paper provides a novel method that extracts observable flight modes from flight test data and uses them in the identification process. For this purpose, a gray-box time-domain method is employed with the nonlinear ARX structure and the Levenberg–Marquardt parameter estimation technique. In the proposed method, the components of the flight parameters are extracted by two signal decomposition techniques, namely the singular spectrum analysis and empirical mode decomposition. These components are inputted into the identification process. Flight test data of the active aeroelastic wing is examined by the proposed method. The results indicate that both the singular spectrum analysis-based and empirical mode decomposition-based identification processes have desired performances in dealing with nontrained flight conditions. Thus, these methods may be utilized as an interpolation method to estimate the aircraft flight dynamics within the flight envelope. However, the empirical mode decomposition outperforms the singular mode decomposition because it is more significant in decomposing flight parameters based on the frequency content. Hence, the empirical mode decomposition may be a better tool to be employed for the aeroelastic aircraft system identification.

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Mathematical model of one flexible transport category aircraft

Cited by 5 publications

References 33 publications

A Detailed Physical Explanation of an Aircraft Flutter Mechanism

A Detailed Physical Explanation of an Aircraft Flutter Mechanism

Nonlinear aeroelastic modeling of aircraft using support vector machine method

Flight dynamics modeling of elastic aircraft using signal decomposition methods

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