Optimal Design of Airfoil with High Aspect Ratio in Unmanned Aerial Vehicles
Shape optimization of the airfoil with high aspect ratio
of long endurance unmanned aerial vehicle (UAV) is performed by the
multi-objective optimization technology coupled with computational
fluid dynamics (CFD). For predicting the aerodynamic characteristics
around the airfoil the high-fidelity Navier-Stokes solver is employed
and SMOGA (Simple Multi-Objective Genetic Algorithm), which is
developed by authors, is used for solving the multi-objective
optimization problem. To obtain the optimal solutions of the design
variable (i.e., sectional airfoil profile, wing taper ratio and sweep) for
high performance of UAVs, both the lift and lift-to-drag ratio are
maximized whereas the pitching moment should be minimized,
simultaneously. It is found that the lift force and lift-to-drag ratio are
linearly dependent and a unique and dominant solution are existed.
However, a trade-off phenomenon is observed between the lift-to-drag
ratio and pitching moment. As the result of optimization, sixty-five
(65) non-dominated Pareto individuals at the cutting edge of design
spaces that is decided by airfoil shapes can be obtained.
[1] H. C. Hwang and K.J. Yoon, "2004 International MAV Competition and
Analysis for the MAV Technologies", Journal of KSAS(Korean), 2004.
[2] S.A. Cambone, K.J. Krieg, P. Pace, and W. Linton, "Unmanned Aircraft
Systems Roadmap 2005-2030," Office of the Secretary of Defense, 2005.
[3] D. Schawe, C.H. Rohardt, and G. Wichmann, "Aerodynamic design
assessment of Strato 2C and its potential for unmanned high altitude
airbone platforms," Aerospace Science and Technology No. 6, 2002,
pp43-51.
[4] Z. Goraj, "Design challenges associated with development of a new
generation UAV," Aircraft Engineering and Aerospace Technology: An
International Journal, Vol. 77, No. 5, 2005, pp.361-368.
[5] T. G. Grabowski, A. Frydrychewicz, Z. Goraj and Suchodolski, "MALE
UAV design of an increased reliability level," Aircraft Engineering and
Aerospace Technology: An international Journal, Vol. 78, No. 3, 2006,
pp 226-235.
[6] S. Painchaud-Ouellet, C. Tribues, J.Y. Trepanier, and D. Pelletier,
"Airfoil Shape Optimization Using a Nonuniform Rational B-Splines
Parametrization Under Thickness Constraint," AIAA Journal, Vol. 44, No.
10, 2006, pp. 2170-2178.
[7] D.J. Pines and F. Bohorquez,., "Challenges Facing Future Micro-Air-
Vehicle Development," Journal of Aircraft, Vol. 43, No. 2, 2006,
pp.290-305.
[8] T.T.H. Ng and G.S.B. Leng, "Application of genetic algorithms to
conceptual design of a micro-air-vehicle," Engineering Applications of
Artificial Intelligence, Vol 15, 2002, pp439-445.
[9] A.S. Fraser, "Simulation of genetic systems," Journal of Theoretical
Biology, 1962, pp. 329-349.
[10] J.H. Holland, Adaptation in Natural and Artificial Systems: an
Introductory Analysis with Applications to Biology, Control, and
Artificial Intelligence, MIT Press, Cambridge, 1975.
[11] D. Goldberg, Genetic Algorithms in Search, Optimization and Machine
Learning, Addision-Wesley, 1989.
[12] A.C. Poloni, A. Giurgevich, L. Onesti, and V. Pediroda, "Hybridization of
a Multi-Objective Genetic Algorithm, a Neural Network and a Classical
Optimizer for a Complex Design Problem in Fluid Dynamics",
Dipartimento di Energetica Universita di Trieste, Italy, 1999.
[13] L.B. Booker, Improving Search in Genetic Algorithms," in Davis L
(Editor), Genetic Algorithms and Simulated Annealing, Morgan
Kaufmann Publishers, Los Altos, CA 1987.
[14] J. Lee, S. Lee, and K. Park, "Global Shape Optimization of Airfoil Using
Multi-Objective Genetic Algorithm," Transaction of KSME B, Vol. 29.
No. 10, 2005, pp. 1163-1171.
[15] STAR-CD v3.20 Methodology, Computational Dynamics, Co., London.
U. K, 2004
[16] K.W. McAlister and R.K. Takahashi, "NACA0015 Wing Pressure and
Trailing Vortex Measurements," NASA Technical Paper 3151, November
1991.
[17] K. Dejong, "An Analysis of the Behavior of a Class of Genetic Adaptive
Systems," Doctoral Thesis, Department of Computer and Communication
Sciences, University of Michigan, Ann Arbor, 1975
[18] J.D. Anderson, Jr, Aircraft Performance and Design, McGraw-Hill, 1999,
Chap 2.
[1] H. C. Hwang and K.J. Yoon, "2004 International MAV Competition and
Analysis for the MAV Technologies", Journal of KSAS(Korean), 2004.
[2] S.A. Cambone, K.J. Krieg, P. Pace, and W. Linton, "Unmanned Aircraft
Systems Roadmap 2005-2030," Office of the Secretary of Defense, 2005.
[3] D. Schawe, C.H. Rohardt, and G. Wichmann, "Aerodynamic design
assessment of Strato 2C and its potential for unmanned high altitude
airbone platforms," Aerospace Science and Technology No. 6, 2002,
pp43-51.
[4] Z. Goraj, "Design challenges associated with development of a new
generation UAV," Aircraft Engineering and Aerospace Technology: An
International Journal, Vol. 77, No. 5, 2005, pp.361-368.
[5] T. G. Grabowski, A. Frydrychewicz, Z. Goraj and Suchodolski, "MALE
UAV design of an increased reliability level," Aircraft Engineering and
Aerospace Technology: An international Journal, Vol. 78, No. 3, 2006,
pp 226-235.
[6] S. Painchaud-Ouellet, C. Tribues, J.Y. Trepanier, and D. Pelletier,
"Airfoil Shape Optimization Using a Nonuniform Rational B-Splines
Parametrization Under Thickness Constraint," AIAA Journal, Vol. 44, No.
10, 2006, pp. 2170-2178.
[7] D.J. Pines and F. Bohorquez,., "Challenges Facing Future Micro-Air-
Vehicle Development," Journal of Aircraft, Vol. 43, No. 2, 2006,
pp.290-305.
[8] T.T.H. Ng and G.S.B. Leng, "Application of genetic algorithms to
conceptual design of a micro-air-vehicle," Engineering Applications of
Artificial Intelligence, Vol 15, 2002, pp439-445.
[9] A.S. Fraser, "Simulation of genetic systems," Journal of Theoretical
Biology, 1962, pp. 329-349.
[10] J.H. Holland, Adaptation in Natural and Artificial Systems: an
Introductory Analysis with Applications to Biology, Control, and
Artificial Intelligence, MIT Press, Cambridge, 1975.
[11] D. Goldberg, Genetic Algorithms in Search, Optimization and Machine
Learning, Addision-Wesley, 1989.
[12] A.C. Poloni, A. Giurgevich, L. Onesti, and V. Pediroda, "Hybridization of
a Multi-Objective Genetic Algorithm, a Neural Network and a Classical
Optimizer for a Complex Design Problem in Fluid Dynamics",
Dipartimento di Energetica Universita di Trieste, Italy, 1999.
[13] L.B. Booker, Improving Search in Genetic Algorithms," in Davis L
(Editor), Genetic Algorithms and Simulated Annealing, Morgan
Kaufmann Publishers, Los Altos, CA 1987.
[14] J. Lee, S. Lee, and K. Park, "Global Shape Optimization of Airfoil Using
Multi-Objective Genetic Algorithm," Transaction of KSME B, Vol. 29.
No. 10, 2005, pp. 1163-1171.
[15] STAR-CD v3.20 Methodology, Computational Dynamics, Co., London.
U. K, 2004
[16] K.W. McAlister and R.K. Takahashi, "NACA0015 Wing Pressure and
Trailing Vortex Measurements," NASA Technical Paper 3151, November
1991.
[17] K. Dejong, "An Analysis of the Behavior of a Class of Genetic Adaptive
Systems," Doctoral Thesis, Department of Computer and Communication
Sciences, University of Michigan, Ann Arbor, 1975
[18] J.D. Anderson, Jr, Aircraft Performance and Design, McGraw-Hill, 1999,
Chap 2.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:60569", author = "Kyoungwoo Park and Ji-Won Han and Hyo-Jae Lim and Byeong-Sam Kim and Juhee Lee", title = "Optimal Design of Airfoil with High Aspect Ratio in Unmanned Aerial Vehicles", abstract = "Shape optimization of the airfoil with high aspect ratio
of long endurance unmanned aerial vehicle (UAV) is performed by the
multi-objective optimization technology coupled with computational
fluid dynamics (CFD). For predicting the aerodynamic characteristics
around the airfoil the high-fidelity Navier-Stokes solver is employed
and SMOGA (Simple Multi-Objective Genetic Algorithm), which is
developed by authors, is used for solving the multi-objective
optimization problem. To obtain the optimal solutions of the design
variable (i.e., sectional airfoil profile, wing taper ratio and sweep) for
high performance of UAVs, both the lift and lift-to-drag ratio are
maximized whereas the pitching moment should be minimized,
simultaneously. It is found that the lift force and lift-to-drag ratio are
linearly dependent and a unique and dominant solution are existed.
However, a trade-off phenomenon is observed between the lift-to-drag
ratio and pitching moment. As the result of optimization, sixty-five
(65) non-dominated Pareto individuals at the cutting edge of design
spaces that is decided by airfoil shapes can be obtained.", keywords = "Unmanned aerial vehicle (UAV), Airfoil, CFD,Shape optimization, Lift-to-drag ratio.", volume = "2", number = "4", pages = "494-7", }
