Multi-objective aerodynamic shape optimization using MOGA coupled to advanced adaptive mesh refinement

• Published in: Computers & fluids Vol. 88; pp. 298 - 312
• Main Authors: Kouhi, Mohammad, Lee, Dong Seop, Bugeda, Gabriel, Oñate, Eugenio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2013
• Subjects: Adaptive remeshing; Aerodynamics; Airfoils; Computational efficiency; Design optimization; Drag; Euler equation; Lift; Mathematical models; MOGA; Reconstruction/multi-objective optimization; Shape optimization; Reconstruction/multi-objective optimization; MOGA; Euler equation; Adaptive remeshing; Shape optimization
• ISSN: 0045-7930; 1879-0747
• DOI: 10.1016/j.compfluid.2013.08.015

•We couple MOGA and adaptive remeshing approach.•It is implemented to three practical aerodynamic shape design problems.•The quality of uniform mesh and adaptive mesh results are compared.•Adaptive remeshing approach accelerates the optimization process.•Increasing the solution accuracy is another benefit of adaptive remeshing approach. This paper demonstrates the big influence of the control of the mesh quality in the final solution of aerodynamic shape optimization problems. It aims to study the trade-off between the mesh refinement during the optimization process and the improvement of the optimized solution. This subject is investigated in the transonic airfoil design optimization using an Adaptive Mesh Refinement (AMR) technique coupled to Multi-Objective Genetic Algorithm (MOGA) and an Euler aerodynamic analysis tool. The methodology is implemented to solve three practical design problems; the first test case considers a reconstruction design optimization that minimizes the pressure error between a predefined pressure curve and candidate pressure distribution. The second test considers the total drag minimization by designing airfoil shape operating at transonic speeds. For the final test case, a multi-objective design optimization is conducted to maximize both the lift to drag ratio (L/D) and lift coefficient (Cl). The solutions obtained with and without adaptive mesh refinement are compared in terms of solution improvement and computational cost. Numerical results clearly show that the use of adaptive mesh refinement can improve the solution accuracy while reducing significant computational cost in both single- and multi-objective design optimizations.