Hostname: page-component-5c6d5d7d68-pkt8n Total loading time: 0 Render date: 2024-08-06T23:51:05.109Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  12 January 2017

S. Saderla ,
R. Dhayalan  and
A.K. Ghosh
S. Saderla*
Affiliation:
School of Aerospace and Software Engineering, Gyeongsang National University, Jinju, South Korea
R. Dhayalan
Affiliation:
Department of Aerospace Engineering, Indian Institute of Space Science and Technology, Trivandrum, India
A.K. Ghosh
Affiliation:
Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, India
*
subrahmanyam.saderla@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

high angle of attackdelta wingUAVflight testsparameter estimationML methodNeural Networks
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1237 , March 2017 , pp. 320 - 340
Copyright
Copyright © Royal Aeronautical Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Jategaonkar, R.V. Flight vehicle system identification—a time domain methodology, AIAA, 2006, 216, Reston, Virginia, US.Google Scholar
2. Hamel, P.G. and Jategaonkar, R.V. Evolution of flight vehicle system identification, J Aircraft, AIAA, 1996, 33, (1), pp 9–28, US.Google Scholar
3. Hamel, P.G. Aircraft parameter identification methods and their applications—survey and future aspects, AGARD-LS-104, Pap. 1, 1979.Google Scholar
4. Klein, V. and Morelli, E.A. Aircraft system identification—theory and practice, AIAA, 2006, Reston, Virginia, US.Google Scholar
5. Goman, M.G., Khrabrov, A.N., Goman, M. and Khrabrov, A.N. State-space representation of aerodynamic characteristics of an aircraft at high , J Aircraft, 1994, 31, (5), pp 1109–1115.CrossRefGoogle Scholar
6. Leishman, J.G. and Nguyen, K.Q. State-space representation of unsteady airfoil behavior, AIAA J, 1990, 28, (5), pp 836–844.Google Scholar
7. Nelson, , R.C. and Pelletier, A. The unsteady aerodynamics of slender wings and aircraft undergoing large amplitude maneuvers, Progress in Aerospace Sciences, 2003, 39, (2–3), pp 185–248.Google Scholar
8. Anton, N., Botez, R.M. and Popescu, D. Stability derivatives for X-31 delta-wing aircraft validated using wind tunnel test data, Proceedings of the Institution of Mechanical Engineers, Vol. 225, Part G, J Aerospace Engineering, 2011, pp 403–416.Google Scholar
9. De Jesus Mota, S. and Botez, R.M. New helicopter model identification method based on a neural network optimization algorithm and on flight test data, Aeronautical J, 2011, 115, (1167), pp 295–314.Google Scholar
10. Boëly, N., Botez, R.M. and Kouba, G. Identification of a nonlinear F/A-18 model by use of fuzzy logic and neural network methods, Proceedings of the Institution of Mechanical Engineers, Part G, J of Aerospace Engineering, 2011, 225, (5), pp 559–574, doi:10.1177/2041302510392871.Google Scholar
11. Boëly, N. and Botez, R.M. New approach for the identification and validation of a nonlinear F/A-18 model by use of neural networks, IEEE Transactions on Neural Networks, 2010, 21, (11), pp 1759–1765.Google Scholar
12. Fischenberg, D. and Jategaonkar, R.V. Identification of aircraft stall behavior from flight test data, 20th Atmospheric and Flight Mechanics Conference, 1995, Baltimore, Maryland, US, pp 138–146.Google Scholar
13. Chowdhary, G. and Jategaonkar, R. Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter, Aerospace Science and Technology, 2010, 14, (2), pp 106–117.Google Scholar
14. Ghoreyshi, , M. and Cummings, R.M. Unsteady aerodynamics modeling for aircraft maneuvers: A new approach using time-dependent surrogate modeling, Aerospace Science and Technology, 2014, 39, pp 222–242.Google Scholar
15. Sugar Gabor, O., Simon, A., Koreanschi, A. and Botez, R.M. Improving the UAS-S4 Éhecatl airfoil high angle of attack performance characteristics using a morphing wing approach, Proceedings of the Institution of Mechanical Engineers, Part G: J Aerospace Engineering, 2016, 23, (2), pp 118–131, doi:10.1177/0954410015587725.Google Scholar
16. Kumar, R. and Ghosh, A. K. Nonlinear modeling of cascade fin aerodynamics using Kirchhoff's steady-stall model, J Aircraft, 2012, 49, (1), pp 315–319.Google Scholar
17. Kumar, , R., Misra, A. and Ghosh, A.K. Effect of gap-to-chord ratio on nonlinear modeling of cascade fin aerodynamics, ICTACEM-10, December 2010, IIT Kharagpur, India.Google Scholar
18. Kumar, R., Misra, A. and Ghosh, A.K. Modelling of cascade fin aerodynamics near stall using Kirchhoff's steady-stall model, Defence Science J, India, 2011, 61, (2)), pp 157–164.Google Scholar
19. Kumar, , R. and Ghosh, A.K. Nonlinear aerodynamic modeling of Hansa-3 aircraft using neural Gauss-Newton method, J Aerospace Science and Technology, 2011, 63, (3), pp 194–204.Google Scholar
20. Peyada, , N.K. and Ghosh, A.K. Aircraft parameter estimation using new filtering technique based on neural network and Gauss-Newton method, Aeronautical J, 2009, 113, (1142), pp 243–252.Google Scholar
21. Dhayalan, R. Parameter Estimation of Flight Vehicles using Conventional and Neural-Based Methods, PhD Thesis, 2015, Indian Institute of Technology Kanpur.Google Scholar
22. Saderla, S. Parameter Estimation using Flight Data of Unmanned Flight Vehicles at Low and Moderately High Angles of Attack using Conventional Methods, PhD thesis, 2015, Indian Institute of Technology Kanpur.Google Scholar
23. Saderla, S., R, D. and Ghosh, A.K. Longitudinal parameter estimation from real flight data of unmanned cropped delta flat plate configuration, Int J Intelligent Unmanned Systems, 2016, 4, (1), pp 2–22.Google Scholar
24. Mehra, R.K., Stepner, D.E. and Tyler, J.S. Maximum likelihood identification of aircraft stability and control derivatives, J Aircraft, 1974, 11, (2), pp 81–89.Google Scholar
25. Kumar, R. and Ghosh, A.K. Parameter estimation using unsteady downwash model from real flight data of Hansa-3 aircraft, Aeronautical J, 2011, 115, (1171), pp 577–588.Google Scholar
26. Napolitano, M.R. Aircraft Dynamics: From Modeling to Simulation, 2012, John Wiley & Sons.Google Scholar
27. Chandra, B., Gupta, R. and Sharma, G. National wind tunnel facility, IIT Kanpur—calibration aspects, Recent Advances in Experimental Mechanics, 2000, pp 294–307.Google Scholar
28. Saderla, S., Rajaram, D. and Ghosh, A. Parameter estimation of unmanned flight vehicle using wind tunnel testing and real flight data. J Aerosp Eng, 2016, 10.1061/(ASCE)AS.1943-5525.0000679, 04016078.Google Scholar