This research represents an innovative method for geometry generation of the squealer-tip in axial compressor rotors and its exploit in a numerical optimization process to obtain a better stage performance. For this purpose, the NASA Rotor-67 transonic compressor rotor blade is used as a test case to study the aerodynamic performance using computational fluid dynamics. The validation was performed for the characteristic map at the design speed and the comparison with the experimental results indicates excellent matching and high adaptability of the numerical method. An ingenious method of producing squealer tip for an axial compressor rotary blade is presented in this article, which is capable for locally shaping both suction and pressure surface geometry at a desired spanwise location simultaneously, while keeping the tip clearance at its value of the baseline NASA Rotor-67 geometry. In this method, control points are used to produce the starting spanwise location of the squealer, and modify the depth of the squealer geometry. The L-27 orthogonal array of the Taguchi method as the Design of Experiment (DOE) has been used to investigate the sensitivity of the aerodynamic results in three performance points of the choke, design and near stall regions, in relation to the design variables of the squealer. The generated database in the sensitivity analysis was used to train artificial neural networks to replace the CFD solutions with overwhelming run time. By coupling the genetic algorithm to the aforementioned neural networks and by applying penalties to maintain the minimum performance of the Rotor-67, enhancement of total pressure ratio, adiabatic efficiency, mass flow rate and even the surge margin was achieved. The main effect of the squealer is to modify the shape of blade tip vortices, and by more dissipation of energy in blade tip area and reduced equivalent flow area in this region, finally results in improved overall mass flow rate, total pressure ratio, adiabatic efficiency and surge margin by 0.58 %, 0.36 %, 0.19 % and 4.81 % respectively, at design point.
Keywords: Axial compressor; Optimization; Performance map; Squealer-tip; Tip leakage.
© 2023 The Authors.