This work proposes an optimization strategy for the preliminary stage of turbomachinery design, based on the application of a throughflow code in the context of a fully automated optimization strategy. The code solves for the circumferentially-averaged flow equations, including the effects of aerodynamic and friction forces and of blade thickness; the outcome of the code is the flow distribution on the meridional surface. In this research the definition of an ‘optimal’ mean flow surface is the final aim of a multi-objective optimization procedure, in which non-concurrent goals are simultaneously considered. Evolutionary algorithms are used in the global optimization process, based on the coupling of a Genetic Algorithm with a metamodel.
The optimization method is applied to a low speed axial compressor, for which the optimization goals are the minimization of aerodynamic loss and discharge kinetic energy at the exit of the stage, as well as the uniformity of work exchange along the blade span. The method proves to match all the objectives, providing a clear improvement with respect to classical and well-established design methods.