This paper presents an optimization procedure for axial-flow ventilation fan design through a hybrid multiobjective evolutionary algorithm (MOEA) coupled with a response surface approximation (RSA) surrogate model. Numerical analysis of a preliminary fan design is conducted by solving three-dimensional (3-D) Reynolds-averaged Navier-Stokes (RANS) equations with the shear stress transport (SST) turbulence model. The multiobjective optimization processes are performed twice to understand the coupled effects of diverse variables. The first multiobjective optimization process is conducted with three design variables defining stagger angles at the hub, mid-span, and tip, and the second is conducted with five design variables defining hub-to-tip ratio, hub cap installation distance, hub cap ratio, and the stagger angles at the mid-span and tip. Two aerodynamic performance parameters, the total efficiency and total pressure rise, are selected as the objective functions for optimization. These objective functions are numerically assessed through 3-D RANS analysis at design points sampled by Latin hypercube sampling in the design space. The optimization yields a maximum increase in efficiency of 1.8% and a 31.4% improvement in the pressure rise. The off-design performance is also improved in most of the optimum designs except in the region of low flow rate.

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