A Strategy for Multi-Point Compressor Blading Using Multi-Objective Optimization

Abstract

This paper introduces a framework to perform a multi-objective multipoint aerodynamic optimization for an axial compressor blade. This framework considers through-flow design requirements and mechanical and manufacturing constraints. Typically, components of a blade design system include geometry generation tools, optimization algorithms, flow solvers, and objective functions. In particular, optimization algorithms and objective functions are tuned to reduce blade design calculation cost and to match designed blade performance to the through flow design criteria and mechanical and manufacturing constrains. In the present study, geometry parameters of blade are classified to three categories. For each category, a distinct optimization loop is applied. In outer loop, Gradient-based optimization techniques are used to optimize parameters of the second category and a two-dimensional compressible viscous flow code is used to simulate the cascade fluid flow. Surface curvature optimization is carried out in inner loop, and its objective function is defined by integrating the normalized curvature and curvature slope. The genetic algorithm is used to optimize the parameters in the interior loop. To highlight the capabilities of the design method and to develop design know-how, an initial profile is optimized with three different design philosophies. The highest performance improvement in the first case is 15% reduction in loss at design incidence angle. In the second case, 16.5% increase in allowable incidence angle range, improves blade’s performance at off design conditions.