2020/02/04 by Hang Zhao, Zhao, Hang
Decision Sciences · Engineering · Mathematics · #Cavitation Phenomena in Pumps #FOS: Computer and information sciences #FOS: Electrical engineering #Grey System Theory Applications #Hydraulic and Pneumatic Systems #Machine Learning (stat.ML) #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2002.02402
13 pages, 13figures
arxiv created 2020/02/04 · openalex publication_date 2020/02/04 · arxiv updated 2021/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A multi-objective prediction method of multi-stage pump method based on neural network with data augmentation is proposed. In order to study the highly nonlinear relationship between key design variables and centrifugal pump external characteristic values (head and power), the neural network model (NN) is built in comparison with the quadratic response surface model (RSF), the radial basis Gaussian response surface model (RBF), and the Kriging model (KRG). The numerical model validation experiment of another type of single stage centrifugal pump showed that numerical model based on CFD is quite accurate and fair. All of prediction models are trained by 60 samples under the different combination of three key variables in design range respectively. The accuracy of the head and power based on the four predictions models are analyzed comparing with the CFD simulation values. The results show that the neural network model has better performance in all external characteristic values comparing with other three surrogate models. Finally, a neural network model based on data augmentation (NNDA) is proposed for the reason that simulation cost is too high and data is scarce in mechanical simulation field especially in CFD problems. The model with data augmentation can triple the data by interpolation at each sample point of different attributes. It shows that the performance of neural network model with data augmentation is better than former neural network model. Therefore, the prediction ability of NN is enhanced without more simulation costs. With data augmentation it can be a better prediction model used in solving the optimization problems of multistage pump for next optimization and generalized to finite element analysis optimization problems in future.