In this paper, a multi-objective optimization of a transonic axial fan to enhance aerodynamic stability has been conducted using three-dimensional Reynolds-Averaged Navier-Stokes equations, surrogate modeling and multi-objective genetic algorithm (MOGA). Hub radius and first rotor chord length of the axial fan were chosen as design variables for the optimization. Peak adiabatic efficiency of the axial fan and stall margin at 60% of the designed rotational speed, were used as objective functions. Latin Hypercube Sampling (LHS) method was used to select design points in the design space. The objective functions were formulated using the response surface approximation (RSA) model. Three LHS samples with different distributions of twelve design points were tested to study their effects on prediction accuracy of the RSA model and optimization results. MOGA with the RSA models based on the best LHS sample, was used to obtain the Pareto-optimal front. As a result of optimization, an improvement of 17.2% in the stall margin at 60% of the designed rotational speed and 2.96% in peak adiabatic efficiency were obtained compared to the reference design. It was also found that distribution of the design points generated by LHS affects the effectiveness of the surrogate-based optimization.

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