Cascade airfoil design by multiobjective genetic algorithms

Multiobjective genetic algorithm based on Fonseca-Fleming's Pareto-based ranking and fitness sharing techniques has been applied to aerodynamic shape optimization of cascade airfoil design. Airfoil performance is evaluated by a Navier-Stokes code. Evaluation of GA population is parallelized on numerical wind tunnel - a parallel vector machine. The present multiobjective design seeks high pressure rise, high flow turning angle, and low total pressure loss at a low Mach number. Pareto solutions that perform better than existing control diffusion airfoil were obtained.