Optimized Wing Design of Tandem-Wing Aircraft Using Microbial Genetic Algorithm and Aerodynamic Performance Analysis Software XFLR5

Aircraft development is a complex process involving multiple layers of design and evaluation. Model-based developments that accomplish design tasks in some of these layers through computer simulations have been proposed to achieve cost reduction. This study presents one such model-based approach that automates the optimization of multiple design parameters by combining microbial genetic algorithm (MGA) and XFLR5, an aerodynamic performance evaluation tool for aircraft. To this end, a Python program was developed and applied to the optimization of the wing configuration of a small tandem-wing aircraft. Four design parameters, namely the mounting angles of the front and rear wings, and horizontal and vertical distances between the wings were successfully and simultaneously optimized. The results demonstrated that the proposed method can contribute to automation and cost reduction of aircraft development, which needs to consider numerous design parameters.