Aerodynamic Shape Optimisation of Unmanned Aerial Vehicles using Hierarchical Asynchronous Parallel Evolutionary Algorithms

One of the challenges in Unmanned (Combat) Aerial Vehicles (UCAV) is the improvement of aerodynamic performance to complete diverse missions, increase endurance and lower fuel consumption. Recent advances in design tools, materials, electronics and actuators have opened the door for implementation of transonic flow control technologies to improve aerodynamic efficiency. This paper explores the application of a robust Multi-Objective Evolutionary Algorithm (MOEA) for the design and optimisation of aerofoil sections and wing planform of UAVs and UCAVs. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. For the design and optimisation of UCAV wing planform shape, an aero-diamond planform shape with a jagged trailing edge is considered like saw tooth. Results obtained from the combination between the approach and the aerodynamic analysis tools show the improvement of the aerodynamic efficiency, a set of shock-free aerofoils and the supercritical aero-diamond wing. Results also indicate that the method is capable to produce non-dominated solutions.

[1]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[2]  Daisuke Sasaki,et al.  Multiobjective evolutionary computation for supersonic wing-shape optimization , 2000, IEEE Trans. Evol. Comput..

[3]  Andreas Zell,et al.  Median-Selection for Parallel Steady-State Evolution Strategies , 2000, PPSN.

[4]  David W. Zingg,et al.  Optimization of Long-Endurance Airfoils , 2003 .

[5]  Viktoria Schmitt,et al.  Pressure distributions on the ONERA M6 wing at transonic Mach numbers , 1979 .

[6]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[7]  A. Vicini,et al.  A Multiobjective Approach to Transonic Wing Design by Means of Genetic Algorithms , 2000 .

[8]  Jacques Periaux,et al.  Advances in Hierarchical, Parallel Evolutionary Algorithms for Aerodynamic Shape Optimisation , 2002 .

[9]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[10]  I. H. Abbott,et al.  Theory of Wing Sections , 1959 .

[11]  Alexander Bolonkin,et al.  Estimated Benefits of Variable-Geometry Wing Camber Control for Transport Aircraft , 1999 .

[12]  W. Stadler Multicriteria Optimization in Engineering and in the Sciences , 1988 .

[13]  Jack Dongarra,et al.  PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing , 1995 .

[14]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[15]  O Allison Dennis,et al.  Airfoil Modification Effects on Subsonic and Transonic Pressure Distributions and Performance for the EA-6B Airplane , 1995 .

[16]  Michael de la Maza,et al.  Book review: Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz (Springer-Verlag, 1992) , 1993 .

[17]  John R. Koza,et al.  Genetic Programming II , 1992 .

[18]  N.A.V. Piercy,et al.  Aerodynamics for Engineers , 1979 .

[19]  Siegfried Wagner,et al.  NUMERICAL OPTIMISATION OF ADAPTIVE TRANSONIC AIRFOILS WITH VARIABLE CAMBER , 2000 .

[20]  George W. Stimson,et al.  Introduction to Airborne Radar , 1983 .

[21]  John E. Renaud,et al.  OPTIMIZED UNMANNED AERIAL VEHICLE WITH WING MORPHING FOR EXTENDED RANGE AND ENDURANCE , 2002 .

[22]  Levent Gurel,et al.  Validation through comparison: Measurement and calculation of the bistatic radar cross section of a stealth target , 2003 .