A machine-learning approach to predicting the energy conversion performance of centrifugal pump impeller influenced by blade profile. (December 2021)

• Record Type: Journal Article
• Authors: Wu, Yanzhao; Tao, Ran; Zhu, Di; Yao, Zhifeng; Xiao, Ruofu
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 24(2021)
• Page Start: 7363
• Page End: 7384
• Publication Date: 2021-12
• Subjects: Flow energy conversion -- neural network -- centrifugal pump -- blade profile -- computational fluid dynamics (CFD) -- machine learning; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/09544062211028264
• Languages: English
• ISSNs: 0954-4062

Abstract

Centrifugal pump is a kind of energy conversion machine for fluid delivering. It transfers the mechanical energy of impeller to the potential and kinetic energy of fluid. As a key factor in influencing the energy conversion performance of centrifugal pump, blade profile design is crucial. Traditional design concepts have ideal assumptions. To have a better design guidance, machine-learning based on neural network is used in this study. A typical centrifugal pump with simplified blade profile is numerically studied with experimental validation for a better discussion. Statistical results show that, for the high dimensional nonlinear relationship between blade angle and performance of centrifugal pump, neural network can adapt to this complex correlation better. The blade installation angle at leading-edge ( βLE ′ ) and trailing-edge ( βTE ′ ) and the wrap angle (Δ θ′ ) has significant correlation with the performance including pump head H, pump efficiency η, impeller head Himp, impeller efficiency ηimp and volute loss Δ Hvol . The influence level of blade angle follows the high-to-low order of Δ θ′, βLE ′ and βTE ′ . Determination of blade profile can be done for improving the energy conversion efficiency. Optimal blade profiles have higher βLE ′ and Δ θ′ with better flow-control ability. Compared with the blade parameters of the initial pump, the blade profile with the best centrifugal pump efficiency is the best βLE ′ increased by 1.926°, Δ θ′ increased by 9.858°, Optimization of impeller efficiency βLE ′ increased by 1.855°, Δ θ′ increased by 9.421°. Computational fluid dynamics indicate the elimination of vortex in impeller after optimal selection. Then, βTE ′ and Δ θ′ are found influential in aggravating the circumferential flow component in this special circular-volute with generating higher loss. βTE ′ has a positive correlation with impeller head which suits traditional theory. In general, the machine-learning using neural network is effective in determining blade profiles for enhancing the performance of centrifugal pump.