Modeling and multi-objective optimization of forward-curved blade centrifugal fans using CFD and neural networks
A Khalkhali, M Farajpoor… - Transactions of the …, 2011 - cdnsciencepub.com
A Khalkhali, M Farajpoor, H Safikhani
Transactions of the Canadian Society for Mechanical Engineering, 2011•cdnsciencepub.comIn the present study, multi-objective optimization of Forward-Curved (FC) blade centrifugal
fans is performed in three steps. In the first step, Head rise (HR) and the Head loss (HL) in a
set of FC centrifugal fan is numerically investigated using commercial software NUMECA.
Two meta-models based on the evolved group method of data handling (GMDH) type neural
networks are obtained, in the second step, for modeling of HR and HL with respect to
geometrical design variables. Finally, using the obtained polynomial neural networks, multi …
fans is performed in three steps. In the first step, Head rise (HR) and the Head loss (HL) in a
set of FC centrifugal fan is numerically investigated using commercial software NUMECA.
Two meta-models based on the evolved group method of data handling (GMDH) type neural
networks are obtained, in the second step, for modeling of HR and HL with respect to
geometrical design variables. Finally, using the obtained polynomial neural networks, multi …
In the present study, multi-objective optimization of Forward-Curved (FC) blade centrifugal fans is performed in three steps. In the first step, Head rise (HR) and the Head loss (HL) in a set of FC centrifugal fan is numerically investigated using commercial software NUMECA. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained, in the second step, for modeling of HR and HL with respect to geometrical design variables. Finally, using the obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto based optimization of FC centrifugal fans considering two conflicting objectives, HR and HL.
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