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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 (H R ) and the Head loss (H L ) 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 H R and H L 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, H R and H L .
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Khalkhali et al. (2011) studied this question.
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