Adaptive POD surrogate model method for centrifugal pump impeller flow field reconstruction based on clustering algorithm

Abstract

To reduce the calculation cost and improve the accuracy of flow field prediction, an adaptive proper orthogonal decomposition (APOD) surrogate model based on K-means clustering algorithm was proposed to reconstruct the flow field of impeller. The experiment samples were designed by introducing the perturbation of the blade control parameters such as blade wrap angle and blade angle of outlet. K-means clustering algorithm was used to classify the sample blade shapes, and find out the cluster of the objective blade. The snapshot set, which consisted of the blade shape and the flow field data of impeller, can be described as a linear combination of orthogonal basis by POD method. The radial basis function (RBF) was used to fit the orthogonal basis coefficients of the objective blade, and then the flow field of objective impeller was reconstructed. The traditional fixed sample POD (FPOD) method and the proposed APOD method were used to reconstruct the flow field in impeller, respectively, and the prediction results of the two methods were compared and analyzed. The results show that the proposed APOD method could quickly and accurately reconstruct the objective flow field. The flow field prediction accuracy of the APOD method is significantly higher than the FPOD method, and the calculation time for the flow field prediction is less than 1/360 of the CFD.