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

TitleAssessment of a Neural-Network-Based Optimization Tool: a Low Specific-Speed Impeller Application
Publication TypeJournal Article
Year of Publication2011
AuthorsCheccucci M, Sazzini F, Marconcini M, Arnone A, Coneri M, De Franco L, Toselli M
JournalInternational Journal of Rotating Machinery
NumberID 817547
ISSN Number1023-621X
Other NumbersScopus 2-s2.0-80053614257
KeywordsArtificial Neural Networks, Centrifugal Pump, Optimization

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.

Refereed DesignationRefereed