Multiple parameters and target optimization of splitter blades for axial spiral blade blood pump using computational fluid mechanics, neural networks, and particle image velocimetry experiment

Abstract

The blood pump is an implantable device with strict performance requirements. Any effective structural improvement will help to improve the treatment of patients. However, the research of blood pump structure improvement is a complex optimization problem with multiple parameters and objectives. This study takes the splitter blade as the object of structural improvement. Computational fluid mechanics and neural networks are combined in research and optimization. And hydraulic experiments and micro particle image velocimetry technology were used. In the optimization study, the number of blades, axial length and circumferential offset are optimization parameters, and hydraulic performance and hemolytic prediction index are optimization targets. The study analyzes the influence of each parameter on performance and completes the optimization of the parameters. In the results, the optimal parameters of number of blades, axial length ratio, and circumferential offset are 2.6° and 0.41°, respectively. Under optimized parameters, hydraulic performance can be significantly improved. And the results of hemolysis prediction and micro particle image velocimetry experiments reflect that there is no increase in the risk of hemolytic damage. The results of this study provide a method and ideas for improving the structure of the axial spiral blade blood pump. The established optimization method can be effectively applied to the design and research of axial spiral blade blood pumps with complex, high precision, and multiple parameters and targets.