Development, Validation, and Application of an Optimization Scheme for Impellers of Centrifugal Fans Using Computational Fluid Dynamics-Trained Metamodels

Abstract

A quick method for the design of efficiency-optimal centrifugal fan impellers is presented. It is based on an evolutionary optimization algorithm that identifies the optimal geometrical parameters for a given aerodynamic objective function. The range of the geometrical parameters considered allows covering aerodynamic design points appropriate for the complete class of centrifugal fans. The quickness of the method stems from evaluating the objective function using metamodels. In total, four metamodels, based on local model networks (LMN) and multi-layer perceptrons (MLP), were trained and eventually aggregated to reduce the variance (stochastic) error. The training data consist of approximately 4000 characteristic curves obtained from automated numerical steady-state Reynolds-averaged Navier–Stokes (RANS) flow simulations. The computational domain as well as the number of grid nodes and their distribution in the domain were optimized in a pre-study. For verification, a grid independence study was carried out. In addition, two criteria were defined to detect aerodynamic operating points associated with non-physical performance predictions. Finally, validation was secured with experimental data from three exemplary impeller designs. The proposed optimization scheme requires a costly initial one-time computational fluid dynamics (CFD) effort, but then allows a quick design of centrifugal fan impellers for arbitrary design points. The search for an optimal centrifugal impeller requires less than 1 min on a standard personal computer, while allowing up to 105 objective function evaluations for one search. Moreover, predicted performance curves that always come along with each design were found to be very reliable in comparison with experiments.