Multi-Objective optimization of an IGV for a large axial fan based on NSGA-II

Abstract

Large axial fans are widely used in industrial refrigeration, building ventilation, and other scenarios because they have inlet guide vanes (IGVs) and outlet guide vanes (OGVs) to make their operation economical and efficient. However, unreasonable guide vane structure is prone to flow separation, forming a tail vortex and interfering with other blades, thereby resulting in the deterioration of working conditions and large noise. Taking a large axial fan with both IGVs and OGVs as the research object, this paper analyzed the structural variables of the fan by global sensitivity analysis method and determine the target of settings optimization as IGV according to the analysis results and the internal flow characteristics of the fan. Furthermore, IGV was parametrically designed, and Kriging model was established and optimized by the second non-dominated sorting genetic algorithm (NSGA-II). Eventually, total pressure and sound pressure level (SPL) were used as targets for multi-objective optimization. The results indicated that the noise produced by the optimized fan was reduced by 6.4 dB and total pressure was increased by 156.4 Pa, with an increase of 0.86% in the total pressure efficiency at the rated working condition, which proved the reliability of the proposed method. This paper also provides a valuable reference for the optimization of similar fluid equipment.