A multi-objective optimization of a transonic axial compressor with circumferential casing grooves has been carried out in the present study. A hybrid multi-objective evolutionary algorithm coupled with response surface approximation is used to optimize the stall margin and design speed efficiency of the transonic axial compressor. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for the flow analysis. The stall margin and peak adiabatic efficiency are used as objective functions for the optimization. Tip clearance and angle distribution at blade tip are considered as design variables in addition to the depth of the circumferential casing grooves which was more sensitive variable than the width in the previous work (GT2010-22396). Latin-hypercube sampling as design-of-experiments is used to generate twenty five design points within the design space. A fast non-dominated sorting genetic algorithm with an ε–constraint strategy for the local search is applied to determine the global Pareto-optimal solutions. The trade-off between two objectives is determined and discussed with respect to the representative clusters in the Pareto-optimal solutions compared to the smooth casing.

This content is only available via PDF.
You do not currently have access to this content.