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论文类型：期刊论文

发表时间：2022-06-29

发表刊物：风机技术

期号：5

页面范围：43-47

ISSN号：1006-8155

摘要：This paper presents an optimization procedure based on a artificial neural network surrogate model for design of for axial-flow cooling fan impeller. Numerical analysis of air-flow in the impeller has been carried out by solving three-dimensional Reynolds-averaged Navier-Stokes equations with the Spalar-Allmaras turbulence model. The optimization processes has been conducted with three design variables defining the inlet angle, the outlet angle of medial camber line of blade and the setting angle of blade. The efficiency and the static pressure rise as aerodynamic performance parameters have been selected as the objective function for optimizations. The objective function values have been assessed through three-dimensional flow analysis at design points sampled by Random among Discrete Levels sampling in the design space. The optimization processes have been performed many times with the different ranges of design variables. Compared with the original model, the optimization design result shows that the efficiency has improved 1.5% and the static pressure rises 87 Pa respectively. The off-design performance has been also improved in all of the optimum shapes, which meets design requirements.

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