Hostname: page-component-6898cfcb55-kwdml Total loading time: 0 Render date: 2026-11-11T11:02:22.994Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust geometric sizing of a small flying wing planform based on evolutionary algorithms

Published online by Cambridge University Press:  27 January 2016

H. Rodríguez-Cortés and
A. Arias-Montaño
H. Rodríguez-Cortés*
Affiliation:
CINVESTAV-IPN, Col San Pedro Zacatenco, Mexico
A. Arias-Montaño
Affiliation:
Instituto Politécnico Nacional, Colonia San José Ticoman, Mexico
*
hrodriguez@cinvestav.mx
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

In this paper a geometric sizing method for a small electric powered flying wing is proposed. The geometric sizing method aims to reduce the effects of variations in the power plant characteristics on endurance. This results in a single-objective design optimisation problem where the sensitivity to power plant characteristics of the endurance equation is minimised, constrained to Reynolds number, wing load, wing taper ratio, aircraft size and wing sweep angle. As a result, geometric characteristics of the flying wing such as span, tip chord and root chord are obtained. Flying wing aerodynamic characteristics are obtained by means of an inviscid fluid flow analysis program of the type low-order panel methods, known as CMARC. The optimisation problem involves a non convex function so that it is necessary to rely on heuristic programming methods. In particular an Evolutionary Algorithm based on differential evolution is considered.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 116 , Issue 1176 , February 2012, pp. 175 - 188
Copyright
Copyright © Royal Aeronautical Society 2012 

References

1. Taguchi, G. The System of Experimental Design: Engineering Methods to Optimise Quality and Minimise Costs, 1987. UNIPUB/Kraus International Publications, New York, USA and American Supplier Institute. Dearborn, MI, USA.Google Scholar
2. Von Mises, R. Theory of Flight, Dover Publications, 1959, New York, USA.Google Scholar
3. Aerologic Inc. PSW: Personal Simulation Works. Available from: http://www.aerologic.com (Accessed 31/03/2011).Google Scholar
4. Grasmeyer, J.M. and Keennon, M.T. Development of the black widow micro air vehicle. In: 39th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, USA, January 2000, paper no. AIAA-2001-0127.Google Scholar
5. Harmon, F.G., Frank, A.A. and Chattot, J. Conceptual design and simulation of a small hybrid-electric unmanned aerial vehicle, J Aircr, 2006, 43, (5), pp 1490–1498.Google Scholar
6. Moffit, B.A., Bradley, T.H., Mavris, D. and Parekh, D. Design Space Exploration of Small-Scale PEM Fuel Cell Long Endurance Aircraft. In: Proceedings of the 6th AIAA Aviation Technology, Integration and Operations Conference, 2006, Wichita, Kansas, USA.Google Scholar
7. Al-Widyn, K. and Angeles, J. A Model-Based Formulation of Robust Design, ASME J Mechanical Design, 2005, 3, (127), pp 388–396.Google Scholar
8. Reyes, E. and Rodriguez, H. Basic Small Fixed-Wing Aircraft Sizing Optimizing. In: Proceedings of the 4th IEEE International Conference on Electrical and Electronics Engineering, 2007, Mexico City, México.Google Scholar
9. Venkataraman, P. Low Speed Multi point Airfoil Design. In: Proceedings of the 16th Applied Aerodynamics Conference, 1998, Albuquerque NM, USA.Google Scholar
10. Etkin, B. and Reid, L.D. Dynamics of Flight: Stability and Control, 1996, 3rd ed, John Wiley and Sons, Toronto, Canada.Google Scholar
11. MaxCim Motors, Brushless DC Motors and Digital Speed Controllers; Design Guidelines for Electric Powered Model Aircraft, Available from: http://www.maxcim.com/guide.html [Accessed 20/08/2007].Google Scholar
12. Mueller, T.J. Aerodynamic Measurements at Low Reynolds Numbers for Fixed Wing Micro-Air Vehicles. RTO AVT/VKI Special Course, 1999, VKI Belgium.Google Scholar
13. Drela, M., Propeller/Windmill Analysis and Design, Available from: http://web.mit.edu/drela/Public/web/qprop/ [Accessed 15/01/2009].Google Scholar
14. Kenneth, V.P., Rainer, M.S. and Jouni, A.L. Differential evolution. A practical Approach to Global Optimisation, Springer, Berlin, Germany, 2005.Google Scholar