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An automated three-dimensional multi-objective optimization and data mining method is presented by integrating a self-adaptive multi-objective differential evolution algorithm (SMODE), 3D parameterization method for blade profile and meridional channel, Reynolds-averaged Navier–Stokes (RANS) solver technique and data mining technique of self-organizing map (SOM). Using this method, redesign of a high pressure ratio centrifugal impeller is conducted. After optimization, 16 optimal Pareto solutions are obtained. Detailed aerodynamic analysis indicates that the aerodynamic performance of the optimal Pareto solutions is greatly improved. By SOM-based data mining on optimized solutions, the interactions among objective functions and significant design variables are analyzed. The mechanism behind parameter interactions is also analyzed by comparing the data mining results with the performance of typical designs.

The design of high pressure ratio impellers is a challenging task. SRV2-O, a typical high pressure ratio centrifugal impeller is selected for the research. A good understanding of flow characteristics in the passage of SRV2-O is obtained by using 3D Reynolds-Averaged Navier-Stokes (RANS) solutions upon numerical validation. It confirms that tip leakage flow and shock wave boundary layer interactions produce the primary energy loss in this transonic impeller. A 3D multi-objective aerodynamic optimization and data mining method named BMOE is presented and programmed by integrating a self-adaptive multi-objective differential evolution algorithm SMODE, 3D blade parameterization method based on non-uniformed B-Spline, RANS solver technique and self-organization map (SOM) based data mining technique. Using BMOE, multi-objective aerodynamic design optimization and data mining is performed for SRV2-O. 14 Pareto solutions are obtained for maximizing isentropic efficiency and total pressure ratio of the impeller. Three typical Pareto solutions, Design A with the highest efficiency, Design B with the higher efficiency and larger pressure ratio and Design C with the maximum pressure ratio, are analyzed. Detailed analysis indicates that the aerodynamic performance of optimized designs is greatly improved. Furthermore, by SOM-based data mining on optimization results, trade-off relation between objective functions and parameter influence mechanism on impeller aerodynamic performance are visualized and explored.

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