Aerodynamic optimization of turbomachinery cascades using Euler/boundary-layer coupled genetic algorithms

A new methodology is developed to find the optimal aerodynamic performance of a turbine cascade. A boundary-layer coupled Euler algorithm and a genetic algorithm are linked within an automated optimization loop. The multiparameter objective function is based on the blade loading. For a given inlet Mach number and baseline cascade geometry, the flow inlet and exit angles, the blade thickness and the solidity are optimized by a robust genetic algorithm. First, the Sanz subcritical turbine cascade is selected as the baseline cascade and is used for How solver validation. Second, the baseline cascade parameters are modified to yield the maximum tangential blade force. Finally, the effects of different crossover techniques, random number seeds, and population sizes on the performance of the genetic algorithm are studied. It is shown that the maximum blade loading is achieved for a higher flow turning, a wider pitch, and a thicker cascade