Aero-structural optimisation of unmanned aerial vehicles using a multi-objective evolutionary algorithm
Gonzalez, Luis F., Damp, Lloyd , & Srinivas, K. (2006) Aero-structural optimisation of unmanned aerial vehicles using a multi-objective evolutionary algorithm. In 2nd Australasian Unmanned Air Vehicles Conference, 19 -22 March 2006, Melbourne.
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This paper describes the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The project looked at the aerodynamics and structure of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The two vehicles wings which were optimised were a High Altitude Long Endurance (HALE) UAV similar to the Global Hawk and a Medium Altitude Long Endurance (MALE) UAV similar to the Altair. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by APMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.
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- Aero-structural optimisation of unmanned aerial vehicles using a multi-objective evolutionary algorithm. (deposited 13 Apr 2007)
- Aero-structural optimisation of unmanned aerial vehicles using a multi-objective evolutionary algorithm. (deposited 14 Aug 2007)[Currently Displayed]
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