Optimization of Discrete Cavities with Guide Vanes in A Centrifugal Compressor based on A Comparative Analysis of Optimization Techniques

Abstract

Discrete cavities with guide vanes were developed and optimized to improve the operating stability of a centrifugal compressor. Various combinations of search algorithms and surrogate models were tested to find the best optimization methods. 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 yaw and pitch angles of the guide vanes and axial distance between cavities were selected as design variables. The stall margin was used as an objective function for the design optimization. Latin hypercube sampling was used to select 27 sample points in the design space. The best combination was found by testing four surrogate models (response surface approximation, Kriging, radial basis neural network, and deep neural network models) and three searching algorithms (a genetic algorithm, particle swarm optimization, and hybrid PSO-GA). Hybrid PSO-GA with the DNN model showed the best overall results. The optimum design showed increments of 13.36% and 3.78% in the stall margin compared to compressors with a smooth casing and the reference cavity design, respectively.