Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data

The paper presents the aerodynamic characterization of a low-speed unmanned aerial vehicle, with cropped delta planform and rectangular cross section, at and around high angles-of-attack using flight test methods. Since the linear models used for identification from flight data at low and moderate angles of attack become unsuitable for accurate parameter estimation at high angles of attack, a non-linear aerodynamic model has to be considered. Therefore, the Kirchhoff's flow separation model was used to incorporate the non-linearity in the aerodynamic model in terms of flow separation point and stall characteristic parameters. The Maximum Likelihood (ML) and Neural Gauss-Newton (NGN) methods were used to perform the parameter estimation on one set of low angle-of-attack and one set of near-stall flight data. It is evident from the estimates that the NGN method, which does not involve solving equations of motion, performs on a par with the classical ML method. This may be attributed to the reason that NGN method uses a neural network which has been trained by performing point to point mapping of the measured flight data. This feature of NGN method enhances its application over a wider envelope of high angles of attack flight data.

[1]  Russell M. Cummings,et al.  Unsteady Aerodynamics Modeling for Aircraft Maneuvers: a New Approach Using Time-Dependent Surrogate Modeling , 2012 .

[2]  R. Kumar,et al.  Parameter estimation using unsteady downwash model from real flight data of Hansa-3 aircraft , 2011 .

[3]  Ajoy Kanti Ghosh,et al.  Longitudinal parameter estimation from real flight data of unmanned cropped delta flat plate configuration , 2016 .

[4]  R. V. Jategaonkar,et al.  Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unscented Kalman Filter , 2010 .

[5]  Ruxandra Mihaela Botez,et al.  New Approach for the Identification and Validation of a Nonlinear F/A-18 Model by Use of Neural Networks , 2010, IEEE Transactions on Neural Networks.

[6]  R. V. Jategaonkar,et al.  Identification of Aircraft Stall Behavior from Flight Test Data , 1999 .

[7]  Ruxandra Botez,et al.  New helicopter model identification method based on flight test data , 2011 .

[8]  D. Fischenberg,et al.  Identification of an Unsteady Aerodynamic Stall Model from Flight Test Data. , 1995 .

[9]  Mikhail Goman,et al.  State-Space Representation of Aerodynamic Characteristics of an Aircraft at High Angles of Attack , 1994 .

[10]  R. V. Jategaonkar,et al.  Evolution of flight vehicle system identification , 1996 .

[11]  Rakesh Kumar,et al.  Modelling of Cascade Fin Aerodynamics Near Stall using Kirchhoff's Steady-state Stall Model , 2011 .

[12]  R. V. Jategaonkar,et al.  Flight Vehicle System Identification: A Time-Domain Methodology, Second Edition , 2015 .

[13]  Ruxandra Botez,et al.  Identification of a non-linear F/A-18 model by the use of fuzzy logic and neural network methods , 2011 .

[14]  Ruxandra Botez,et al.  Stability derivatives for a delta-wing X-31 aircraft validated using wind tunnel test data , 2011 .

[15]  Robert C. Nelson,et al.  The unsteady aerodynamics of slender wings and aircraft undergoing large amplitude maneuvers , 2003 .

[16]  Raman K. Mehra,et al.  Maximum likelihood identification of aircraft stability and control derivatives , 1974 .

[17]  J. Leishman,et al.  State-space representation of unsteady airfoil behavior , 1990 .

[18]  Rakesh Kumar,et al.  Nonlinear Modeling of Cascade Fin Aerodynamics Using Kirchhoff's Steady-State Stall Model , 2012 .

[19]  Ajoy Kanti Ghosh,et al.  Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method , 2009, The Aeronautical Journal (1968).

[20]  Ajoy Kanti Ghosh,et al.  Parameter Estimation of Unmanned Flight Vehicle Using Wind Tunnel Testing and Real Flight Data , 2017 .

[21]  Ruxandra Botez,et al.  Improving the UAS-S4 Éhecal airfoil high angles-of-attack performance characteristics using a morphing wing approach , 2016 .