A. Khalkhali۱ - ۱Department of Mechanical Engineering, Faculty of Engineering, Islamic Azad University, East Tehran Branch, Tehran, Iran
H. Safikhani۲ - ۲Hydraulic Machinery Research Institute, School of Mechanical Engineering, College of Engineering, University of Tehran
M Pour mohammad۳ - ۳Department of Mechanical Engineering, Faculty of Engineering, Islamic Azad University, East Tehran Branch, Tehran, Iran

Increasing of head rise (HR) and decreasing of head loss (HL), simultaneously, are important purpose in the design of different types of fans. Therefore, multiobjective optimization process is more applicable for the design of such turbo machines. In the present study, multi-objective optimization of Forward- Curved (FC) blades centrifugal fans is performed at three steps. At the first step, Head rise (HR) and the Head loss (HL) in a set of FC centrifugal fanis numerically investigated using commercial software NUMECA. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained, at the second step, for modeling of HRandHLwith respect to geometrical design variables. Finally, using obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto based optimization of FCcentrifugal fans considering two conflicting objectives,HR and HL. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of FC fans can be discovered by Pareto based multiobjective optimization of the obtained polynomial meta-models representing their HR and HL 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.

Forward-Curved Blades Centrifugal Fan; Multi-objective optimization; CFD; GMDH; Genetic Algorithms