Optimization of centrifugal artificial heart pump blade parameters based on back propagation neural network and grey wolf optimization algorithm
The impeller,as a key component of artificial heart pumps,experiences high shear stress due to its rapid rotation,which may lead to hemolysis.To enhance the hemolytic performance of artificial heart pumps and identify the optimal combination of blade parameters,an optimization design for existing pump blades is conducted.The number of blades,outlet angle,and blade thickness were selected as design variables,with the maximum shear stress within the pump serving as the optimization objective.A back propagation(BP)neural network prediction model was established using existing simulation data,and a grey wolf optimization algorithm was employed to optimize the blade parameters.The results indicated that the optimized blade parameters consisted of 7 impeller blades,an outlet angle of 25 °,and a blade thickness of 1.2 mm;this configuration achieved a maximum shear stress value of 377 Pa-representing a reduction of 16％compared to the original model.Simulation analysis revealed that in comparison to the original model,regions with high shear stress at locations such as the outer edge,root,and base significantly decreased following optimization efforts,thus leading to marked improvements in hemolytic performance.The coupling algorithm employed in this study has significantly reduced the workload associated with modeling and simulation,while also enhancing the performance of optimization objectives.Compared to traditional optimization algorithms,it demonstrates distinct advantages,thereby providing a novel approach for investigating parameter optimization issues related to centrifugal artificial heart pumps.
万方数据
维普
