Re-Configurable Multi Mission Unmanned Aerial Vehicle (RC-MM-UAV) emerging design concept involves morphing wings with mission segment based airfoils. To identify the appropriate planform, the Direct Numerical Optimization (DNO) methodology is applied in the design. The method requires a flexible geometrical shape function for airfoil parameterisation coupled with a validated computational flow solver for aerodynamic computation. The two sub-systems operate concurrently with an intelligent search agent to obtain an optimal planform. In this study, airfoil shapes are represented with the PARSEC shape function and a low order panel method for quick turn-around time is used to establish the aerodynamic coefficients. A scholastic optimisation technique based on the Particle Swarm theory is proposed that iteratively examines candidate airfoils, until convergence based on user specified objectives and constraints. A single-objective flight condition is examined to test the robustness and flexibility of the DNO set up. An optimum airfoil is defined as a planform with minimum drag performance, thus maximizing the lift-to-drag ratio. Fixed operating conditions include the lift coefficient, angle-of-attack, Reynolds and Mach number. Geometrical constraints are imposed to control the thickness-to-chord ratio of the final airfoil. The obtained results show that the proposed methodology is capable of providing acceptable designs. The use of high-fidelity solvers in comparison to panel methods would make the DNO structure computationally inefficient, thus the use of artificial neural networks is proposed for further analysis in the design of RC-MM-UAV airfoils.


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    Title :

    Multi mission Re-configurable UAV - airfoil optimization through swarm approach and low fidelity solver


    Contributors:


    Publication date :

    2008


    Size :

    13 Seiten, 7 Bilder, 5 Tabellen, 21 Quellen



    Type of media :

    Conference paper


    Type of material :

    Storage medium


    Language :

    English




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