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

Abstract EN

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.