Shape optimization of a cyclone separator using multi-objective surrogate-based optimization

Cyclones are one of the most widely used separators in many industrial applications. A low mass loading gas cyclone has two performance parameters, the Euler and Stokes numbers. These indices are highly sensitive to the geometrical design parameters which makes designing cyclones a challenging problem. This paper couples three surrogate models (Kriging, radial basis functions and support vector regression) with the efficient multi-objective optimization (EMO) algorithm to identify a Pareto front of cyclone designs with a minimal number of simulations. The EMO algorithm has been extended to select multiple samples per iteration (as opposed to one in the original formulation) and the ability to use an ensemble of surrogate models. The impact of using different surrogate model types is tested using well-known mathematical models of cyclone separators. The algorithm is applied to optimize the cyclone geometry, parametrized by seven design variables, and compared against the well-known NSGA-II algorithm. The results indicate that the Pareto set designs found using EMO outperform the designs found using NSGA-II while using significantly fewer function evaluations. This translates into substantial savings in time when computationally expensive CFD simulations are used.