Active Aeroelastic Wing Aerodynamic Model Development and Validation for a Modified F/A-18A

Cumming, Stephen B.; Diebler, Corey

doi:10.2514/6.2005-6312

Cited by 11 publications

(5 citation statements)

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“…29 Based on these flight tests, aerodynamic models describing the elastic behavior of the AAW were developed and verified in some parts of its flight envelope. 30 Also, more experiments were conducted in the Mach range between 0.85 and 1.3 and the altitude from sea level up to 25,000 feet in order to test the aircraft flying quality and evaluate the designed controllers. 30 These tests finally led to the completion and validation of the aeroservoelastic model 31 and the flight dynamics model of the aircraft.…”

Section: Flight Test Datamentioning

confidence: 99%

“…30 Also, more experiments were conducted in the Mach range between 0.85 and 1.3 and the altitude from sea level up to 25,000 feet in order to test the aircraft flying quality and evaluate the designed controllers. 30 These tests finally led to the completion and validation of the aeroservoelastic model 31 and the flight dynamics model of the aircraft. 32 The AAW has two General Electric F404-GE-400 turbofan engines.…”

Section: Flight Test Datamentioning

confidence: 99%

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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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Section: Flight Test Datamentioning

confidence: 99%

Section: Flight Test Datamentioning

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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“…Aerodynamic characterisation enables researchers to develop a mathematical description of a flight vehicle's associated aerodynamics, which can either be a linear or nonlinear function of nondimensional aerodynamic parameters. Quantification of these non-dimensional parameters for various flight envelopes is well explored using estimation methods such as Equation Error Method (EEM) [1][2][3][4][5], Output Error Method (OEM) [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], Filter Error Method (FEM) [23,24] and Artificial Intelligence (AI) method [11,25,26,27,28] from the flight data of manned aircraft for various predefined manoeuvers. However in the case of UAVs, the availability of research related to the aerodynamic characterisation from flight data is minimal due to their classified applications.…”

Section: Introductionmentioning

confidence: 99%

System identification of cropped delta UAVs from flight test methods using particle Swarm-Optimisation-based estimation

2022

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In the era of Unmanned Aerial Systems (UAS), an onboard autopilot occupies a prominent place and is inevitable for many of their modern applications. The efficacy of autopilot heavily relies upon the accuracy of the sensors employed and the capability of the onboard flight controller. In general, aerodynamic behaviour and flight dynamic capabilities of Unmanned Aerial Vehicles (UAVs) govern the selection and the design of flight controllers. Precise modeling of linear aerodynamic characteristics from flight data can be achieved using many of the existing classical parameter estimation techniques such as Output Error Method (OEM), Equation Error Method (EEM), and Filter Error Method (FEM). However, all the classical methods may not be readily applicable for aerodynamic modeling in nonlinear flight envelopes. The current manuscript is an attempt to exploit the capabilities of the Artificial Intelligence (AI) technique, named Particle Swarm Optimisation (PSO), in combination with Least Squares (LS) cost function to perform linear as well as nonlinear aerodynamic parameter estimation. The aforementioned task is accomplished by considering flight data from manoeuvers pertaining to linear angles of attack, moderate and near stall flight envelopes of two different UAVs with cropped delta planform geometry. Parameters estimated using the proposed LS-PSO method are consistent with minimum standard deviation and are on a par with OEM estimates. The proposed LS-PSO method enhances the capabilities of LS-based EEM while estimating stall characteristic parameters, which was not possible with LS alone. The longitudinal and lateral-directional static parameters estimated from the full-scale wind tunnel testing of the two UAVs were also used to corroborate the results obtained from the flight data using the LS-PSO method.

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“…The data from these maneuvers were used to create delta values to the baseline aircraft stability derivatives (ref. 4). Additionally, the maneuvers were used to develop a linear structural loads model to predict wing and control surface structural loads based on aircraft states and control surface deflections.…”

Section: Phase I Flightsmentioning

confidence: 99%