Surrogate-based optimization design and data mining of an axial fan for a refrigerator

The efficiency of the axial fan has a direct impact on the cooling performance of refrigerators, so this paper proposes a design optimization and data mining method based on the surrogate model to improve the efficiency of the axial fan and understand the optimization principle behind it. In this paper, the optimization algorithm based on the Kriging model is applied to optimize the axial fan. The candidate points for each evaluation are set to 5 to improve the optimization efficiency, and the final axial fan efficiency is improved by 2.58%. Then, through data mining technology of the Analysis of Variance (ANOVA), self-organizing map (SOM), and parallel axis analyses of the design space, it is found that the increase of the thickness of the pressure surface of the third flow surface and the decrease of the thickness of the suction surface can make the axial flow fan obtain better aerodynamic performance.