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Adaptive-surrogate-based robust optimization of transonic natural laminar flow nacelle 被引量:3
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作者 Yuan YAO Dongli MA +2 位作者 Muqing YANG Liang ZHANG Yang GUO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2021年第10期36-52,共17页
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.T... 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. 展开更多
关键词 Adaptive surrogate model Aerodynamic robustness Multi-objective optimization natural laminar flow nacelle Uncertain working conditions
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