Multi-objective optimization of groove casing treatment in a transonic compressor

The paper presents a multi-objective optimization of circumferential casing grooves geometries for the NASA Rotor 37 transonic compressor. The depth normalized by the tip clearance and the width normalized by the tip chord are selected as the design variables. The stall margin and peak efficiency are used as the objective functions. The Latin Hypercube Sampling technique was used to select the sample points in the design space. Based on the numerical results of the sample points, the radial basis function network model of the artificial neural network was constructed. The NSGA-II multi-objective evolutionary algorithm is then employed to search for Pareto-optimal solutions. The leave-one-out cross validation method was also used to evaluate the precision of the radial basis function network model. The results of the optimization show the present method can be effectively used for the design of circumferential casing grooves to take account of the stall margin and efficiency. From the Pareto-optimal solutions, two groove configurations are selected and the internal flow fields are compared with the smooth casing. The effect mechanism of the circumferential casing grooves on the performance of the transonic compressor is discussed by the analysis of the flow in the blade tip region.