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

Abstract

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 offdesign conditions compared to the initial blade.

Numerical and Theoretical Investigations Concerning the Continuous-Surface-Curvature Effect in Compressor Blades

“…Unlike the optimization methods in [11][12][13][14][15][16], which aim at building a blade surface with the best aerodynamic performance and result in much alteration of the shape of blade surface, the method suggested in this paper is to get an optimized continuous curvature blade without major modification to the datum blade geometry, which helps to exclude the influence of blade shape alteration when analyzing the effect of curvature continuity. The method for the leading edge and the main surface, respectively, is detailed as follows.…”

“…The small improvement in the performance of each blade row may result in visible accumulated improvement of multistage compressor performance. Secondly, the continuous-curvature optimization method is easy to realize, that is, there is no apparent increase in complexity of the blade representation method prescribing curvature distribution compared with the conventional ones, and meanwhile, it is suggested by Fathi and Shadaram [16] that the curvature-based optimization method can increase the speed of design process because of the closer relationship between design parameters with the flowfield. …”

“…The method has been further extended to compressor blades and reduction of losses inside the boundary layer on the suction surface as well as improvement of blade performance is observed numerically for the optimized compressor blades [14]. Sommer [15] and Fathi [16] also presented surface curvature optimizations for compressor blades. It should be noted that in their research the curvature as well as the gradient of curvature of the blade surface are both continuous, since the curvature distributions are defined by Bezier splines or B-splines.…”

Wing profile evolution driven by computational fluid dynamics

“…Optimization methods use an objective function to be satisfied (e.g. gradient-based method [1,2]). These methods are successful under one or two criteria to achieve a specific effect (e.g.…”

“…Refs. Advantages Disadvantages Evolution from an initial guess, using optimization methods [1,2,3,4,5,6,7,8,12,16,17] (1) Successful to achieve a specific effect.…”