Aircraft Lateral Parameter Estimation from Flight Data with Unsteady Aerodynamic Modeling

1982 ◽  
Vol 19 (3) ◽  
pp. 206-210 ◽  
Author(s):  
William R. Wells ◽  
Siva S. Banda ◽  
David L. Quam
Keyword(s):  
Parameter Estimation ◽  
Flight Data ◽  
Aerodynamic Modeling ◽  
Unsteady Aerodynamic

scholarly journals Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

2016 ◽  
Vol 53 (5) ◽  
pp. 1261-1297 ◽  
Author(s):  
Jay M. Brandon ◽  
Eugene A. Morelli
Keyword(s):  
Real Time ◽  
Flight Data ◽  
Aerodynamic Modeling ◽  
Nonlinear Aerodynamic

Surrogate Unsteady Aerodynamic Modeling with Autoencoders and LSTM Networks

2022 ◽  
Author(s):  
Huseyin E. Tekaslan ◽  
Yusuf Demiroglu ◽  
Melike Nikbay
Keyword(s):  
Aerodynamic Modeling ◽  
Unsteady Aerodynamic

Spin Aerodynamic Modeling for a Fixed-Wing Aircraft Using Flight Data

2022 ◽  
Author(s):  
James L. Gresham ◽  
Benjamin M. Simmons ◽  
Jeremy W. Hopwood ◽  
Craig A. Woolsey
Keyword(s):  
Flight Data ◽  
Aerodynamic Modeling

Unsteady aerodynamic modeling and control of the wind turbine with trailing edge flap

2018 ◽  
Vol 10 (6) ◽  
pp. 063304 ◽  
Author(s):  
Wenguang Zhang ◽  
Yifeng Wang ◽  
Ruijie Liu ◽  
Haipeng Liu ◽  
Xu Zhang
Keyword(s):  
Wind Turbine ◽  
Trailing Edge ◽  
Modeling And Control ◽  
Aerodynamic Modeling ◽  
Unsteady Aerodynamic ◽  
Trailing Edge Flap ◽  
And Control

Physical Essence of Stall Phenomena and its WNN-Based Aerodynamic Modeling from Flight Data

2014 ◽  
Vol 602-605 ◽  
pp. 3140-3143
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Flight Data ◽  
Numerical Examples ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Aerodynamic Method ◽  
Physical Essence ◽  
Set Up

To understand the characteristics of aircraft stall for better aerodynamic model, the physical essence of the stall phenomena of aircraft is first introduced, and then a Wavelet Neural Network (WNN) is proposed to set up the stall aerodynamic model. Numerical examples indicates that through the deep cognition of the stall phenomena of aircraft the proposed stall aerodynamic method has a better accuracy than the traditional neural network and is also effective and feasible.

Drag Coefficient Identification from Flight Data via Optimal Observer

2014 ◽  
Vol 687-691 ◽  
pp. 787-790
Author(s):  
Rong Jun Yang ◽  
Yao Ye
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Drag Coefficient ◽  
Dynamic Equation ◽  
Unscented Kalman Filter ◽  
Flight Test ◽  
Measurement Noise ◽  
Estimation Technique ◽  
Flight Data ◽  
Coefficient Identification

. For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed. The point mass dynamic equation is used to form the Unscented Kalman Filter (UKF) and the smoother (URTSS) for the estimation of a projectile’s flight states. The projectile flight states are then solved and utilized to extract the drag coefficient information using the observer techniques. The simulation verifies the feasibility of the method: with measurement noise, the accurate drag coefficient is obtained by using the smoother.

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