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International Journal of Rotating Machinery
Volume 2011, Article ID 817547, 11 pages
Research Article

Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application

1“Sergio Stecco” Department of Energy Engineering, University of Florence, Via di Santa Marta 3, 50139 Firenze, Italy
2Termomeccanica Pompe S.p.A, Via del Molo 3, 19126 La Spezia, Italy

Received 30 November 2010; Accepted 7 May 2011

Academic Editor: Ken Ichi Funazaki

Copyright © 2011 Matteo Checcucci et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work provides a detailed description of the fluid dynamic design of a low specific-speed industrial pump centrifugal impeller. The main goal is to guarantee a certain value of the specific-speed number at the design flow rate, while satisfying geometrical constraints and industrial feasibility. The design procedure relies on a modern optimization technique such as an Artificial-Neural-Network-based approach (ANN). The impeller geometry is parameterized in order to allow geometrical variations over a large design space. The computational framework suitable for pump optimization is based on a fully viscous three-dimensional numerical solver, used for the impeller analysis. The performance prediction of the pump has been obtained by coupling the CFD analysis with a 1D correlation tool, which accounts for the losses due to the other components not included in the CFD domain. Due to both manufacturing and geometrical constraints, two different optimized impellers with 3 and 5 blades have been developed, with the performance required in terms of efficiency and suction capability. The predicted performance of both configurations were compared with the measured head and efficiency characteristics.