Adaptive-surrogate-based robust optimization of transonic natural laminar flow nacelle

Natural Laminar Flow (NLF) technology is very effective for reducing the skin friction drag of aircraft engine nacelle, but the aerodynamic performance of NLF nacelle is highly sensitive to uncertain working conditions. Therefore, it's imperative to incorporate uncertainties into the design of NLF nacelle. In this study, for a robust optimization of NLF nacelle and for improving its efficiency, an adaptive-surrogate-based robust optimization strategy is established, which is an iterative optimization process where the surrogate model is updated to obtain the real Pareto front of multi-objective optimization problem. A case study is carried out to validate its feasibility and effectiveness. The results show that the optimization increases the favorable pressure gradient region and the volume ratio of the nacelle by increasing its lip radius and reducing its maximum diameter. And the aerodynamic robustness of the NLF nacelle is mainly determined by the lip radius, maximum diameter of nacelle and location of the maximum diameter. Compared to the initial nacelle, the optimized nacelle maintains a wide range of low drag and high laminar flow ratio in the disturbance space, which extends the average laminar flow region to 21.6% and facilitates a decrease of 1.98 counts in the average drag coefficient.