Robust Optimization of Natural Laminar Flow Airfoil Based on Random Surface Contamination

Natural laminar-flow (NLF) airfoils are one of the most promising technologies for extending range and endurance aircrafts. However, there is a lack methods optimization based on surface contamination that destroys laminar flow. In order to solve this problem, robust process proposed using Non-dominated Sorting genetic algorithm- II (NSGA-II) evolutionary algorithm, Monte Carlo simulation combined with an aerodynamic calculation software Xfoil. Firstly, airfoil optimized normally performance under analyzed. Then, original robustly random assumption its locations follow triangular uniform probability distributions. Finally, all results compared. It found reduces sensitivity contamination, hence, improving robustness airfoil. The make it possible improve NLF considering contamination.