Flight dynamics modeling of elastic aircraft using signal decomposition methods

Bagherzadeh, Seyed Amin

doi:10.1177/0954410018821788

Cited by 3 publications

(3 citation statements)

References 33 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Majeed and Vikalp [11] build a neural model of an aircraft from flight data and online estimation of the aerodynamic derivatives from the established neural model. Bagherzadeh [12] provided a novel method that extracts observable flight modes from flight test data and uses them in the identification process by conducting a gray box time domain method. Wu and Chen [13] developed an online system identification method for tiltrotor aircraft flight dynamic modeling by establishing a weighted recursive least squares algorithm.…”

Section: Introductionmentioning

confidence: 99%

Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data

Li,

Chen,

Zhang

et al. 2023

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Stochastic disturbances are everywhere. The influence of stochastic factors on the modeling and simulation of aircraft flight dynamics should be considered. Therefore, a stochastic differential equation for aircraft flight attitude is modeled based on the traditional one in this paper. After that, an identification method based on the idea of sparse recognition for unknown parameters and stochastic disturbance is proposed. Finally, a set of measured flight data is used to verify that the identified stochastic model has obvious advantages over the traditional deterministic model when the aircraft is maneuvering in flight. This method can improve the accuracy and reliability of the aircraft flight dynamic model.

show abstract

Section: Introductionmentioning

confidence: 99%

Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data

Li,

Chen,

Zhang

et al. 2023

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

show abstract

“…Many time and frequency domain decomposition methods have been developed until now; nevertheless, time–frequency methods are the most appropriate techniques for analyzing nonstationary signals (Shafi et al, 2009; Wang et al, 2018; Zhu et al, 2012). Among the time–frequency methods, the empirical mode decomposition (EMD) has outstanding characteristics such as being data-driven and capability of decomposing nonlinear and nonstationary signals (Bagherzadeh, 2018; Bagherzadeh, 2019; Bagherzadeh and Sabzehparvar, 2015; Bagherzadeh and Asadi, 2017; Bagherzadeh et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Analysis of in-flight cabin vibration of a turboprop airplane by proposing a novel noise-tolerant signal decomposition method

Bagherzadeh

Salehi

2021

Journal of Vibration and Control

View full text Add to dashboard Cite

Vibration in passenger cabins of turboprop airplanes is a serious challenge. One of the essential steps in studying the cabin vibrations is to determine the contributing sources of vibration. The vibration signals are highly nonstationary and noisy. Therefore, one may require a noise-tolerant signal processing method for decomposition of the signals. In this article, the wavelet-based empirical mode decomposition is introduced for the first time to improve the performance of the traditional empirical mode decomposition in dealing with noise. Unlike the traditional empirical mode decomposition that extracts the signal trend by averaging the upper and lower envelopes intersecting local maxima and minima of the signal, the wavelet-based empirical mode decomposition directly extracts the signal trend by applying the multilevel wavelet decomposition of the consecutive approximations within the sifting process. Numerical studies are undertaken to evaluate the effect of noise on the performance of the empirical mode decomposition and wavelet-based empirical mode decomposition. Also, comparisons are made between the methods at dissimilar noise powers based on the orthogonality, integral, and energy decomposition criteria. The results indicate that both methods generate similar results in the absence of noise. Considering the number of obtained intrinsic mode functions, decomposition quality criteria, and computational cost, however, the wavelet-based empirical mode decomposition outperforms the classic method at higher noise levels. In this article, the wavelet-based empirical mode decomposition is used for analysis of in-flight airplane cabin vibration. A 52-passenger turboprop aircraft is equipped with eight triaxial piezoelectric accelerometers, and several flight tests are performed to acquire in-flight vibration signals within the passenger cabin. The proposed wavelet-based empirical mode decomposition is applied to the experimental data. Then, the amplitudes and frequencies of the intrinsic mode functions are examined. Finally, the probable vibration sources are identified based on the intrinsic mode functions characteristics.

show abstract

“…With the rapid development of numerical methods and computer science and technology, computational fluid dynamics (CFD) has been widely used to investigate the aerodynamic performance and flight dynamic characteristics of various projectiles, 13–15 while computational structural dynamics (CSD) technique has been usually adopted to solve the structural deformation. When the load-carrying structure is complicated or when large geometric deformation exists, it is difficult to obtain accurate structural deformation based on simplified beam/shell model.…”

Section: Introductionmentioning

confidence: 99%

Trimming analysis of flexible aircrafts based on computational fluid dynamics/computational structural dynamics coupling methodology

Hua

Chen

Yuan

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

A numerical methodology based on the coupling of computational fluid dynamics (CFD) and computational structural dynamics is established to obtain the trimming characteristics of flexible aircrafts in this paper. Reynolds-averaged Navier–Stokes equations are solved through CFD technique. Based on the frame of unstructured mesh, techniques of dynamic chimera mesh and morphing mesh are adopted to treat the data transfer between different computational zones and structure deformation caused by aeroelasticity, respectively. When it is applied to a projectile model with large slenderness ratio constructed in this paper, convergence histories of various initial conditions demonstrate the efficiency and robustness of the algorithm. The influence of the structural rigidity and normal loads on the trimming condition of flexible projectiles is investigated, and the locations of the aerodynamic center with various rigidities present the explanation that elastic deformation can move the aerodynamic center forward and weaken the margin of the stability. Furthermore, the trimming condition of flexible projectiles with propulsion is researched, which indicates that thrust misalignment will increase the effect of elastic deformation on the trimming condition, and the stability margin will be further weakened because of thrust misalignment. The conclusion provided in this paper can provide guidance for the structural design, control system design, and stability analysis for modern aircrafts with small stability margin and low rigidity.

show abstract

Flight dynamics modeling of elastic aircraft using signal decomposition methods

Cited by 3 publications

References 33 publications

Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data

Identification of a Stochastic Dynamic Model for Aircraft Flight Attitude Based on Measured Data

Analysis of in-flight cabin vibration of a turboprop airplane by proposing a novel noise-tolerant signal decomposition method

Trimming analysis of flexible aircrafts based on computational fluid dynamics/computational structural dynamics coupling methodology

Contact Info

Product

Resources

About