Wing Planform Optimization Using Flow Solutions and Response Surface Methodology

Yildirim B. Y. , TUNCER İ. H.

AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021, Virtual, Online, 2 - 06 August 2021 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.2514/6.2021-2564
  • City: Virtual, Online


© 2021, American Institute of Aeronautics and Astronautics Inc.. All rights reserved.In this study, a wing planform optimization is performed by using 3D flow solutions together with the response surface methodology. The purpose of this study is to demonstrate an optimization approach involving aerodynamic shapes of aircraft. For this purpose, the wing planform of a turboprop trainer aircraft is optimized to achieve the lowest possible drag coefficient while ensuring the desired maneuvering capability and lateral stability. The optimization objective and constraints are determined considering mission requirements and dimensions of turboprop trainer aircraft already operating. Flow solutions are obtained by using a 3D open-source RANS solver, SU2, to calculate aerodynamic coefficients required in the objective function and constraints. Instead of using flow solutions to calculate aerodynamic coefficients at each optimization iteration, surrogate models that relate design parameters to be optimized to the objective function and constraints are used. Surrogate models are constructed as high-order nonlinear analytical functions with the help of response surface methodology. Sequential experimentation, a design of experiment technique, is used to determine design points where flow solutions are obtained to construct surrogate models. Surrogate models are validated by comparing the results of models with flow solutions and validated surrogate models are used in the optimization. Since the objective function and constraints are nonlinear functions, nonlinear optimization algorithms are used. The optimization is performed by using different optimization algorithms, the sequential quadratic programming, and the interior point, and different initial conditions to observe the effect of the optimization algorithm and the initial condition on the optimum configuration. The optimum wing planform obtained is compared with the initial configuration in terms of the objective function value, and the suitability of the optimum wing planform to the constraints is evaluated.