In order to shorten the design period, the paper describes a new optimization strategy for computationally expensive design optimization of turbomachinery, combined with design of experiment (DOE), response surface mo...In order to shorten the design period, the paper describes a new optimization strategy for computationally expensive design optimization of turbomachinery, combined with design of experiment (DOE), response surface models (RSM), genetic algorithm (GA) and a 3-D Navier-Stokes solver(Numeca Fine). Data points for response evaluations were selected by improved distributed hypercube sampling (IHS) and the 3-D Navier-Stokes analysis was carried out at these sample points. The quadratic response surface model was used to approximate the relationships between the design variables and flow parameters. To maximize the adiabatic efficiency, the genetic algorithm was applied to the response surface model to perform global optimization to achieve the optimum design of NASA Stage 35. An optimum leading edge line was found, which produced a new 3-D rotor blade combined with sweep and lean, and a new stator one with skew. It is concluded that the proposed strategy can provide a reliable method for design optimization of turbomachinery blades at reasonable computing cost.展开更多
Aerodynamic optimization design of compressor blade shape is a design challenge at present because itis inherently a multiobjective problem. Thus, multiobjective Genetic Algorithms based on the multibranch simulated a...Aerodynamic optimization design of compressor blade shape is a design challenge at present because itis inherently a multiobjective problem. Thus, multiobjective Genetic Algorithms based on the multibranch simulated annealing selection and collection of Pareto solutions strategy have been developedand applied to the optimum design of compressor cascade. The present multiobjective design seeks highpressure rise, high flow turning angle and low total pressure loss at a low inlet Mach number. Paretosolutions obtain the better aerodynamic performance of the cascade than the existing Control DiffusionAirfoil. From the Pareto solutions, the decision maker would be able to find a design that satisfies hisdesign goal best. The results indicate that the feasibility of multiobjective Genetic Algorithms as amultiple objectives optimization tool in the engineering field.展开更多
文摘In order to shorten the design period, the paper describes a new optimization strategy for computationally expensive design optimization of turbomachinery, combined with design of experiment (DOE), response surface models (RSM), genetic algorithm (GA) and a 3-D Navier-Stokes solver(Numeca Fine). Data points for response evaluations were selected by improved distributed hypercube sampling (IHS) and the 3-D Navier-Stokes analysis was carried out at these sample points. The quadratic response surface model was used to approximate the relationships between the design variables and flow parameters. To maximize the adiabatic efficiency, the genetic algorithm was applied to the response surface model to perform global optimization to achieve the optimum design of NASA Stage 35. An optimum leading edge line was found, which produced a new 3-D rotor blade combined with sweep and lean, and a new stator one with skew. It is concluded that the proposed strategy can provide a reliable method for design optimization of turbomachinery blades at reasonable computing cost.
文摘Aerodynamic optimization design of compressor blade shape is a design challenge at present because itis inherently a multiobjective problem. Thus, multiobjective Genetic Algorithms based on the multibranch simulated annealing selection and collection of Pareto solutions strategy have been developedand applied to the optimum design of compressor cascade. The present multiobjective design seeks highpressure rise, high flow turning angle and low total pressure loss at a low inlet Mach number. Paretosolutions obtain the better aerodynamic performance of the cascade than the existing Control DiffusionAirfoil. From the Pareto solutions, the decision maker would be able to find a design that satisfies hisdesign goal best. The results indicate that the feasibility of multiobjective Genetic Algorithms as amultiple objectives optimization tool in the engineering field.
