Hostname: page-component-7dd5485656-c8zdz Total loading time: 0 Render date: 2025-10-31T22:44:06.480Z Has data issue: false hasContentIssue false
  • English
  • Français

Numerical and experimental validation of a morphed wing geometry using Price-Païdoussis wind-tunnel testing*

Part of: Smart Aircraft

Published online by Cambridge University Press:  18 May 2016

A. Koreanschi ,
O. Sugar-Gabor  and
R.M. Botez
A. Koreanschi
Affiliation:
Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity LARCASE, École de technologie supérieure ETS, University of Quebec, Montreal, Quebec, Canada
O. Sugar-Gabor
Affiliation:
Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity LARCASE, École de technologie supérieure ETS, University of Quebec, Montreal, Quebec, Canada
R.M. Botez*
Affiliation:
Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity LARCASE, École de technologie supérieure ETS, University of Quebec, Montreal, Quebec, Canada
*
ruxandra@gpa.etsmtl.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

An experimental validation of an optimised wing geometry in the Price-Païdoussis subsonic wind tunnel is presented. Two wing models were manufactured using optimised glass fibre composite and tested at three speeds and various angle-of-attack. These wing models were constructed based on the original aerofoil shape of the ATR 42 aircraft and an optimised version of the same aerofoil for a flight condition of Mach number equal to 0.1 and angle-of-attack of 0°. The aerofoil's optimisation was realised using an ‘in-house’ genetic algorithm coupled with a cubic spline reconstruction routine, and was analysed using XFoil aerodynamic solver. The optimisation was concentrated on improving the laminar flow on the upper surface of the wing, between 10% and 70% of the chord. XFoil-predicted pressure distributions were compared with experimental data obtained in the wind tunnel. The transition position was estimated from the experimental pressure data using a second derivative methodology and was compared with the transition predicted by XFoil code. The results have shown the agreement between numerical and experimental data. The wind-tunnel tests have shown that the improvement of the laminar flow of the optimised wing is higher than the value predicted numerically.

Keywords

AerodynamicsXFoiltransition flowmorphing wingwind-tunnel testsgenetic algorithmsoptimisation

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1227 , May 2016 , pp. 757 - 795
Copyright
Copyright © Royal Aeronautical Society 2016 

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.)

Article purchase

Temporarily unavailable

Footnotes

*

The authors' names were originally presented in the wrong order. A notice detailing this has been published and the error rectified in the online PDF and HTML copies.

References

REFERENCES

1. Patron Felix, S.R., Kessaci, A. and Botez, R.M. Horizontal flight trajectories optimization for commercial aircraft through a flight management system, The Aeronautical j, 2013, 118, (1210), p 201.Google Scholar
2. Patron Felix, S.R. and Botez, R.M. New altitude optimization algorithm for the flight management system CMA-9000 improvement on the A310 and L-1011 aircraft, Aeronautical J, 2013, 177, (1194), pp 787–805.Google Scholar
3. Pecora, R., Barbarino, S., Concilio, A., Lecce, L. and Russo, S. Design and functional test of a morphing high-lift device for a regional aircraft, J Intelligent Material Systems and Structures, 2011, 22, (10), pp 1005–1023.CrossRefGoogle Scholar
4. Diodati, G., Concilio, A., Ricci, S., De Gaspari, A., Liauzun, C. and Godard, J.L. Estimated performance of an adaptive trailing-edge device aimed at reducing fuel consumption on a medium-size aircraft, 8690-14, Smart Structures/NDE Conference, 10-14 March 2013, San Diego, California, US.Google Scholar
5. Pecora, R., Amoroso, F. and Lecce, L. Effectiveness of wing twist morphing in roll control, J Aircr, 2012, 49, (6), pp 1666–1674.CrossRefGoogle Scholar
6. Gamboa, P., Vale, J., Lau, F. and Suleman, A. Optimization of a morphing wing based on coupled aerodynamic and structural constraints, AIAA J, 2009, 47, (9), pp 2087–2104.CrossRefGoogle Scholar
7. Falcão, L., Gomes, A.A. and Suleman, A. Aero-structural design optimization of a morphing wingtip, J Intelligent Material Systems and Structures, 2011, 22, (10), pp 1113–1124.CrossRefGoogle Scholar
8. Sugar Gabor, O., Koreanschi, A. and Botez, R.M. Optimization of an unmanned aerial systems’ wing using a flexible skin morphing wing, SAE Int J Aerospace, 2013, 6, (1), pp 115–121.CrossRefGoogle Scholar
9. Sugar Gabor, O., Simon, A., Koreanschi, A. and Botez, R.M. Application of a morphing wing technology on hydra technologies unmanned aerial system UAS-S4, ASME International Mechanical Engineering Congress and Exposition IMECE14, 14-20 November 2014, Montreal, Canada.CrossRefGoogle Scholar
10. Sofla, A.Y.N., Meguid, S.A., Tan, K.T. and Yeo, W.K. Shape morphing of aircraft wing: Status and challenge, Elsevier J, Material and Design, 2010, 31, (3), pp 1284–1292.Google Scholar
11. Vasista, S., Tong, L. and Wong, K.C. Realizations of morphing wings: A multidisciplinary challenge, J Aircr, 2012, 49, (1), pp 11–28.CrossRefGoogle Scholar
12. Eppler, R. Airfoil Design and Data, 1990, Edition Springer-Verlag, Berlin-Heidelberg GmbH, pp 163–510, ISBN is 978-3-662-02648-9.CrossRefGoogle Scholar
13. Muller, T.J. (Ed). Low Reynolds Number Aerodynamics, Proceedings of the Conference Notre-Dame, 5-7 June 1989, Indiana, US.Google Scholar
14. Popov, A.V., Botez, R.M., Mamou, M., Mebarki, Y., Jahrhaus, B., Khalid, M. and Grigorie, T.L. Drag reduction by improving laminar flows past morphing configurations, AVT-168 NATO Symposium on the Morphing Vehicles, 20-23 April 2009, Evora, Portugal.Google Scholar
15. Botez, R.M., Molaret, P. and Laurendeau, E. Laminar flow control on a research wing project presentation covering a three year period, CASI Aircraft Design and Development Symposium, 25-26 April 2007, Toronto, Ontario, Canada.Google Scholar
16. Grigorie, L.T., Popov, A.V., Botez, R.M., Mamou, M. and Mébarki, Y. A hybrid fuzzy logic proportional-integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy Part 1: Morphing system mechanisms and controller architecture design, Aeronautical J, 2012, 116, (1179), pp 433–449.CrossRefGoogle Scholar
17. Sainmont, C., Paraschivoiu, I., Coutu, D., Brailovski, V., Laurendeau, E., Mamou, M., Mebarki, Y. and Khalid, M. Boundary layer behaviour on a morphing airfoil: Simulation and wind tunnel tests, CASI AERO'09 Conference Aerodynamics Symposium, 2009.Google Scholar
18. Grigorie, L.T., Botez, R.M. and Popov, A.V. Design and experimental validation of a control system for a morphing wing, AIAA Atmospheric Flight Mechanics Conference, Invited Session Paper, 13-17 August 2012, Minneapolis, Minnesota, US.CrossRefGoogle Scholar
19. Grigorie, L.T., Botez, R.M., Popov, A.V., Mamou, M. and Mébarki, Y. A new morphing mechanism for a wing using smart actuators controlled by a self-tuning fuzzy logic controller, AIAA Centennial of Naval Aviation Forum: 100 Years of Achievement and Progress, 20-22 September 2011, Virginia Beach, Virginia, US.Google Scholar
20. Popov, A.V., Botez, R.M., Grigorie, T. L., Mamou, M. and Mebarki, Y. Real time airfoil optimization of a morphing wing in wind tunnel, AIAA J Aircr, 2010, 47, (4), pp 1346–1354.CrossRefGoogle Scholar
21. Popov, A.V., Botez, R. M., Grigorie, T. L., Mamou, M. and Mebarki, Y. Closed loop control of a morphing wing in wind tunnel, AIAA J Aircr, 2010, 47, (4), pp 1309–1317.CrossRefGoogle Scholar
22. Mitchell, M. An Introduction to Genetic Algorithms, 1996, Cambridge, Massachusetts, US, MIT Press.Google Scholar
23. Coley, D.A. An Introduction to Genetic Algorithms for Scientists and Engineers, 1999, World Scientific Publishing, Singapore.CrossRefGoogle Scholar
24. Kulfan, B.M. and Bussoletti, J.E. Fundamental parametric geometry representations for aircraft component shapes, AIAA 2006–6948 Technical Paper, 2006.CrossRefGoogle Scholar
25. Deb, R.A.K. Simulated binary crossover for continuous search space, Complex Systems, 1995, 9, (3), pp 115–148.Google Scholar
26. Drela, M. and Youngren, D. XFOIL Version 6.96 Documentation, 2001, http://web.mit.edu/drela/Public/web/xfoil/xfoil_doc.txt.Google Scholar
27. Drela, M. XFoil: An Analysis and Design System for Low Reynolds Number Airfoils, Low Reynolds Number Aerodynamics, Proceedings of the Conference Notre-Dame, 5-7 June 1989, Indiana, US.Google Scholar
28. Drela, M. An integral boundary layer formulation for blunt trailing edges, AIAA 89-2200 Technical Paper, 1989.CrossRefGoogle Scholar
29. Drela, M. Implicit implementation of the full En transition criterion, Proceedings of 21st Applied Aerodynamics Conference, 23-26 June 2003, Orlando, Florida, US, AIAA 2003–4066.CrossRefGoogle Scholar
30. van Ingen, J.L. The eN method for transition prediction. Historical review of work at TU Delft, AIAA 38th Fluid Dynamics Conference and Exhibit, 23-26 June 2008, Seattle, Washington, US, AIAA 2008–3830.CrossRefGoogle Scholar
31. Ben Mosbah, A., Flores Salinas, M., Botez, R. and Dao, T. New methodology for wind tunnel calibration using neural networks - EGD approach, SAE Int J Aerospace, 2013, 6, (2), pp 761–766, doi:10.4271/2013-01-2285.CrossRefGoogle Scholar
32. Borlow, J., Rae, W. and Pope, A. Low Speed Wind Tunnel Testing, 1999, Wiley, Hoboken, New Jersey, US.Google Scholar
33. Michaud, F., Joncas, S. and Botez, R.M. Design, manufacturing and testing of a small-scale composite morphing wing, 19th International Conference on Composite Materials, 28 July-2 August 2013, Montreal, Quebec, Canada.Google Scholar
34. Calestreme, R. Conception et fabrication de modelés d'aile en composite a fin de les tester dans la soufflerie Price-Païdoussis, Master Project, École de Technologie Supérieure, 2012, Montreal, Quebec, Canada.Google Scholar
35. Storms, B., Takahashi, T.T. and Ross, J.C. Aerodynamic influence of a finite-span flap on a Simpla wing, SAE Technical paper 951977, 1995.CrossRefGoogle Scholar
36. Bastedo, W.G. and Mueller, T.J. Spanwise variation of laminar separation bubbles on wings at low Reynolds numbers, J Aircr, 1986, 23, (9), pp 687–694.Google Scholar
37. Aerolab. Data Acquisition Systems. Available at: http://www.aerolab.com/products/data-acquisition-systems/, accessed in February 2015.Google Scholar
38. Shaw, R. The influence of hole dimensions on static pressure measurements, J Fluid Mechanics, 1960, 7, (4), pp 550–564.CrossRefGoogle Scholar
39. Tavoularis, S. Measurement in Fluid Mechanics, 2005, Cambridge University Press, New York, US.Google Scholar
40. Vercauteren, J., Bosschaerts, W., Baelmans, M. and Persoons, T. Numerical investigation on the measurement error of static pressure taps in small scale channels. Proceedings Power MEMS, Proceedings of the 10th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS), December 1-3 2010, Leuven, Belgium, pp 295–298.Google Scholar
41. Reynolds, O. An experimental investigation of the circumstances which determine whether the motion of water shall be direct or sinuous, and of the law of resistance in parallel channels, Philosophical Transactions of the Royal Society, London, 1883, 174, pp 935–982.Google Scholar
42. Schlichting, H. and Gersten, K. Boundary-Layer Theory, 2000, Springer-Verlag, Berlin, pp 377–378.CrossRefGoogle Scholar
43. Menter, F.R., Langtry, R.B., Likki, S.R., Suzen, Y.B., Huang, P.G. and Völker, S. A correlation-based transition model using local variables – part I: model formulation. Journal of turbomachinery, 2006, 128, (3), pp 413–422.CrossRefGoogle Scholar
44. Silisteanu, P.D. and Botez, R.M. Transition-flow-occurrence estimation: A new method, J Aircr, 2010, 47, (2), pp 703–708.CrossRefGoogle Scholar
45. Granville, P.S. The calculation of the viscous drag of bodies of revolution, David Taylor Model Basin Report 1953, p 849.CrossRefGoogle Scholar
46. Michel, R. Étude de la transition sur les profils d'aile; Éstablissement d'un critére de determination du point de transition et calcul de la trainée de profil en incompressible, ONERA Report 1/1578, 1952.Google Scholar
47. Wazzan, A.R., Gazley, C. and Smith, A.M.O. Tollmien-schlichting waves and transition, Progress in Aerospace Sciences, 1979, 18, (2), pp 351–392.Google Scholar
48. Mamou, M., Yuan, W. and Khalid, M. Transition prediction in low reynolds airfoil flows using finite element/difference solvers coupled with the en method: A comparative study, AIAA Paper 2006–3176, 2006.CrossRefGoogle Scholar
49. Khrabrov, A. and Oi, M.V. Effects of flow separation on aerodynamic loads in linearized thin airfoil theory, J Aircr, 2004, 41, (4), pp 944–948.CrossRefGoogle Scholar
50. Menter, F.R., Langtry, R.B., Likki, S.R., Suzen, Y.B., Huang, P.G. and Volker, S. A correlation-based transition model using local variables – part I: Model formulation, J Turbomachinery, 2006, 128, pp 413–422.CrossRefGoogle Scholar
51. Langtry, R.B., Menter, F.R., Likki, S.R., Suzen, Y.B., Huang, P.G. and Volker, S. A correlation-based transition model using local variables – part II: Test cases and industrial applications, J Turbomachinery, 2006, 128, pp 423–434.Google Scholar
52. Popov, A.V., Botez, R.M. and Labib, M. Transition point detection from the surface pressure distribution for controller design, J Aircr, 2008, 45, (1), pp 23–28.CrossRefGoogle Scholar
53. Grigorie, T.L. and Botez, R.M. New adaptive controller method for SMA hysteresis modelling of a morphing wing, Aeronautical J, 2010, 114, (1151), pp 1–13.CrossRefGoogle Scholar
54. Grigorie, L.T., Popov, A.V., Botez, R.M., Mamou, M. and Mébarki, Y. A hybrid fuzzy logic proportional integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy, part 2: Controller implementation and validation, Aeronautical J, 2012, 116, (1179), pp 451–465.CrossRefGoogle Scholar