关键词: 排气蜗壳；参数化；试验设计；径向基神经网络；气动优化

Abstract: In order to improve the aerodynamic performance of a turbine exhaust hood，an integrated optimization platform based on NUMECA and Isight was developed，and a multivariable optimization method was also explored. After parameterizing the diffusing structure profile of exhaust hood，the optimal Latin hypercube design of experiment was used to obtain an evenly distributed sample space. Besides，the aerodynamic performance of exhaust hood design candidate was evaluated by numerical simulation，after that，radial basis function (RBF) neural networks surrogate model was established. Two optimization methods were used to search for the final optimal resolutions，including Adaptive Simulated Algorithms (ASA) which was a global search approach and Hooke-Jeeves direct search approach. The results show that the aerodynamic performance of optimal exhaust hood is much better than that of original one on design condition without increasing the inlet static pressure，and the total pressure loss coefficient declines by 9.82%，as well as an increase by 12.2% on static pressure recovery coefficient. On the meanwhile，the velocity distribution of outlet becomes more uniform. All of these prove the effectiveness of the optimization system.

Key words: Exhaust hood；Parameterization；Design of experiment；Radial basis function ；Aerodynamic optimization