Multi-Level Multi-Objective Multi-Point Optimization System for Axial Flow Compressor 2D Blade Design

This paper introduces a multi-level framework to perform a multi-objective multi-point aerodynamic optimization of the axial compressor blade. This framework results in a considerable speed-up of the design process by reducing both the design parameters and the computational effort. To reduce the computational effort, optimization procedure is working on two levels of sophistication. Fast but approximate prediction methods has been used to find a near-optimum geometry at the firs-level, which is then further verified and refined by a more accurate but expensive Navier–Stokes solver. Surface curvature optimization was carried out in a first-level as a meta-function. Genetic algorithm and gradient-based optimization were used to optimize the parameters of first-level and second-level, respectively. This procedure considers both the aerodynamic and mechanical constraints. An initial blade has been optimized with three different design targets to highlight the ability of the design method and to develop design know-how. Leading-edge shape and curvature distributions of pressure and suction surface had major effects on the design philosophies of the blades. The result shows about −22.5 % reductions in pressure-loss coefficient at design condition and 23.6 % improvement in the allowable incidence-angle range at off-design conditions compared to the initial blade.