Optimization of centrifugal artificial heart pump blade parameters based on back propagation neural network and grey wolf optimization algorithm_Journal of Biomedical Engineering

Authors：

MU Lulu ¹ , DUAN Huanhuan ^1,2 , XIAO Yuan ¹ ,  CUI Guomin ¹

1. Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. School of Energy and Intelligent Engineering, Henan Institute of Animal Husbandry Economics, Zhengzhou 450000, P. R. China;

Corresponding author：

CUI Guomin, Email: cgm@usst.edu.cn

Keywords：

Artificial heart pump; Blade parameters; Back propagation neural network; Grey wolf optimization algorithm; Hemolytic properties

DOI：

10.7507/1001-5515.202403057

Video：

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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.

Citation： MU Lulu, DUAN Huanhuan, XIAO Yuan, CUI Guomin. Optimization of centrifugal artificial heart pump blade parameters based on back propagation neural network and grey wolf optimization algorithm. Journal of Biomedical Engineering, 2024, 41(6): 1221-1226. doi: 10.7507/1001-5515.202403057 Copy

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14.	戴伟峰. 考虑湍流影响的磁悬浮离心式血泵多目标优化设计研究. 苏州: 苏州大学, 2021.
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18.	Wei Liming, Xv Shuo, Li Bin. Short-term wind power prediction using an improved grey wolf optimization algorithm with back-propagation neural network. Clean Energy, 2022, 6(2): 288-296.

1. Selmi M, Chiu W C, Chivukula V K, et al. Blood damage in Left Ventricular Assist Devices: Pump thrombosis or system thrombosis. Int J Artif Organs, 2019, 42(3): 113-124.
2. Reul H M, Akdis M. Blood pumps for circulatory support. Perfusion, 2000, 15(4): 295-311.
3. Wiegmann L, Boës S, de Zélicourt D, et al. Blood pump design variations and their influence on hydraulic performance and indicators of hemocompatibility. Ann Biomed Eng, 2018, 46(3): 417-428.
4. Li Y, Wang H, Xi Y, et al. Impact of volute design features on hemodynamic performance and hemocompatibility of centrifugal blood pumps used in ECMO. Artif Organs, 2023, 47(1): 88-104.
5. Hosseini S E, Keshmiri A. Experimental and numerical investigation of different geometrical parameters in a centrifugal blood pump. Res Biomed Eng, 2022, 38(2): 423-437.
6. Onder A, Incebay O, Sen M A, et al. Heuristic optimization of impeller sidewall gaps-based on the bees algorithm for a centrifugal blood pump by CFD. Int J Artif Organs, 2021, 44(10): 765-772.
7. 郑建设. 离心泵内部流动数值模拟与优化设计. 沈阳: 沈阳航空航天大学, 2014.
8. 胡佳伟. 离心水泵叶轮工作曲面有限元分析及优化设计. 杨凌: 西北农林科技大学, 2011.
9. 胡英, 汪坤, 胡季. 遗传算法在离心泵叶轮设计中的应用. 昆明理工大学学报(自然科学版), 2014, 39(5): 42-46.
10. 郭涛, 李国君, 田辉, 等. 基于遗传算法的离心泵叶轮参数化造型及优化设计. 排灌机械工程学报, 2010, 28(5): 384-388.
11. Jing T, Sun H, Cheng J, et al. Optimization of a screw centrifugal blood pump based on random forest and multi-objective gray wolf optimization algorithm. Micromachines, 2023, 14(2): 406.
12. 周志华. 机器学习. 北京: 清华大学出版社, 2016: 93.
13. 刘泽辉. 磁悬浮离心式人工心脏泵设计及性能评价. 济南: 山东大学, 2024.
14. 戴伟峰. 考虑湍流影响的磁悬浮离心式血泵多目标优化设计研究. 苏州: 苏州大学, 2021.
15. 李卫东. 离心式人工心脏泵血流动力学特性分析与优化研究. 黑龙江: 哈尔滨工业大学, 2016.
16. Chen C, Xie D, Xiong Y, et al. Optimization of turbine cold-end system based on BP neural network and genetic algorithm. Front Energy, 2014, 8(4): 459-463.
17. Mirjalili S, Mirjalili S M, Lewis A D. Grey wolf optimizer. Adv Eng Softw, 2014, 69(3): 46-61.
18. Wei Liming, Xv Shuo, Li Bin. Short-term wind power prediction using an improved grey wolf optimization algorithm with back-propagation neural network. Clean Energy, 2022, 6(2): 288-296.

Journal of Biomedical Engineering

Optimization of centrifugal artificial heart pump blade parameters based on back propagation neural network and grey wolf optimization algorithm

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