Pareto based multi-objective optimization of centrifugal pumps using CFD, neural networks and genetic algorithms

Abstract

Increase of efficiency (η) and decrease of the required NPSH simultaneously are important objectives in the design of centrifugal pumps. In the present study, multi-objective optimization of centrifugal pumps is performed in three steps. In the first step, η and NPSHr in a set of centrifugal pumps are numerically investigated using commercial software NUMECA. Two meta-models based on the evolved Group Method of Data Handling (GMDH) type neural networks are obtained. The second step is the modeling of η and NPSHr with respect to geometrical design variables. Finally, using obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto based optimization of centrifugal pumps considering two conflicting objectives, η and NPSHr. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of centrifugal pumps can be discovered by Pareto based multi-objective optimization of the obtained polynomial meta-models representing their η and NPSHr characteristics. Such important optimal principles would not have been obtained without the use of both GMDH type neural network modeling and the Pareto optimization approach.