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- W4312921859 abstract "In this paper, the study on aerodynamic shape optimization of contra rotating open rotor is presented. Computational fluid dynamics simulations combined with Neural networks (radial basis function) surrogate model is applied to solve the problem. The difficulty is associated with both time-consuming computational fluid dynamics simulations and the large number of design variables involved. The contra rotating open rotor optimization problem is to find the variables that produces the maximum propulsive efficiency for a limited range of thrust requirement. The DOE (deign of experiment) process is completed by uniform design and computational fluid dynamics simulations. The non-linear programming by quadratic lagrangian (NLPQL) is used to tackle the optimization problem. Infill sampling criteria is used in this process to reduce demand on the number of initial samples while keep the accuracy of surrogate model. After building the initial surrogate from DOE sampling, the update point is selected according to the infill sampling criteria. It shows that the infill sampling criteria is helpful in surrogate model building process and surrogate-based optimization method is valid for aerodynamic shape optimization of contra rotating open rotor." @default.
- W4312921859 created "2023-01-05" @default.
- W4312921859 creator A5050836156 @default.
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- W4312921859 date "2022-04-12" @default.
- W4312921859 modified "2023-10-16" @default.
- W4312921859 title "SURROGATE-BASED AERODYNAMIC SHAPE OPTIMIZATION OF CONTRA ROTATING OPEN ROTOR" @default.
- W4312921859 doi "https://doi.org/10.33737/gpps21-tc-76" @default.
- W4312921859 hasPublicationYear "2022" @default.
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