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Open AccessArticle

Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model

College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225000, China
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Energies 2020, 13(4), 779; https://doi.org/10.3390/en13040779
Received: 1 January 2020 / Revised: 4 February 2020 / Accepted: 6 February 2020 / Published: 11 February 2020
(This article belongs to the Special Issue Mathematical Modelling of Energy Systems and Fluid Machinery)
This study adopts a multi-disciplinary optimization design method based on an approximation model to improve the comprehensive performance of axial-flow pump impellers and fully consider the interaction and mutual influences of the hydraulic and structural designs. The lightweight research on axial-flow pump impellers takes the blade mass and efficiency of the design condition as the objective functions and the head, efficiency, maximum stress value, and maximum deformation value under small flow condition as constraints. In the optimization process, the head of the design condition remains unchanged or varies in a small range. Results show that the mass of a single blade was reduced from 0.947 to 0.848 kg, reaching a decrease of 10.47%, and the efficiency of the design condition increased from 93.91% to 94.49%, with an increase rate of 0.61%. Accordingly, the optimization effect was evident. In addition, the error between the approximate model results and calculation results of each response was within 0.5%, except for the maximum stress value. This outcome shows that the accuracy of the approximate model was high, and the analysis result is reliable. The results provide guidance for the optimal design of axial-flow pump impellers. View Full-Text
Keywords: axial-flow pump; impeller; approximation model; optimization design; multi-disciplinary axial-flow pump; impeller; approximation model; optimization design; multi-disciplinary
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Shi, L.; Zhu, J.; Tang, F.; Wang, C. Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model. Energies 2020, 13, 779.

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