Stability enhancement of a centrifugal compressor using inclined discrete cavities

Abstract

Inclined discrete cavities are proposed to improve the operating stability of a centrifugal compressor while minimizing the flow loss, and optimized to simultaneously maximize the adiabatic efficiency and stall margin. Aerodynamic analysis was performed using three-dimensional Reynolds-averaged Navier–Stokes equations. The numerical results obtained for the total pressure ratio and adiabatic efficiency were validated with experimental data for the centrifugal compressor with a smooth casing. The angle of the cavity port, axial distance between cavities, and inclined angle of the cavity were selected as design variables based on sensitivity analysis. The stall margin and adiabatic efficiency at the design point were used as the objective functions for the design. Latin hypercube sampling was used to select 24 design points in the design space. Two neural network models, that is, the radial basis and deep neural networks, were tested with respect to surrogate models and their performances were compared using statistical analysis. A hybrid PSO–GA algorithm was used to identify the optimal solutions for the surrogate models. The deep neural network model has a better overall performance than the radial basis neural network model. The optimization results show that the stall margin increases, with increments of up to 6.41% and 11.32% compared with the reference design and compressor with a smooth casing, respectively.