Whole flow field performance prediction by impeller parameters of centrifugal pumps using support vector regression

Abstract

The relationship of multiple impeller parameters and performance indices is difficult to describe because of some unknown hydrodynamic phenomena. Modeling of performance indices of the whole flow field from impeller parameters often encounters some challenges, especially lower prediction accuracy in relatively small and large flow points, dependence on designers’ experience and time-consuming designing process. In this work, the least squares support vector regression (LSSVR) method is proposed to predict multiple pump performance indices of the whole flow field. To describe the performance more completely, the powder, the head, and the efficiency indices are chosen as the model outputs. Additionally, to improve the prediction accuracy and reduce the manufacture difficulty, nine impeller parameters and the flow rate are selected as the model inputs. With the LSSVR model, the complex nonlinearity relationship between multiple impeller parameters and performance indices can be described approximately. Moreover, the LSSVR model and the computational fluid dynamics numerical simulation model are applied to predict the powder, the head, and the efficiency of an actual centrifugal mine pump in the whole flow field. Compared with the performance test results, the superiority of the proposed method is demonstrated in terms of more accurate prediction performance and faster designing process.