Optimization of a Screw Centrifugal Blood Pump Based on Random Forest and Multi-Objective Gray Wolf Optimization Algorithm

The centrifugal blood pump is a commonly used ventricular assist device. It can replace part of the heart function, pumping blood throughout the body in order to maintain normal function. However, the high shear stress caused by the impeller rotating at high speeds can lead to hemolysis and, as a co...

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Bibliographic Details
Published in:Micromachines (Basel) Vol. 14; no. 2; p. 406
Main Authors: Jing, Teng, Sun, Haoran, Cheng, Jianan, Zhou, Ling
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.02.2023
MDPI
Subjects:
Algorithms
Analysis
Blood pumps
centrifugal blood pump
Centrifugal pumps
Design
Design optimization
Efficiency
Genetic algorithms
Heart
Heart failure
Heart function
Hemolysis
Impellers
Mathematical optimization
Mortality
multi-objective gray wolf optimization (MOGWO)
Multiple objective analysis
numerical simulation
Optimization algorithms
random forest (RF)
scalar shear stress
Shear stress
Ventricular assist devices
Pennsylvania
scalar shear stress
centrifugal blood pump
multi-objective gray wolf optimization (MOGWO)
hemolysis
numerical simulation
random forest (RF)
ISSN:2072-666X, 2072-666X
Online Access:Get full text
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Summary:The centrifugal blood pump is a commonly used ventricular assist device. It can replace part of the heart function, pumping blood throughout the body in order to maintain normal function. However, the high shear stress caused by the impeller rotating at high speeds can lead to hemolysis and, as a consequence, to stroke and other syndromes. Therefore, reducing the hemolysis level while ensuring adequate pressure generation is key to the optimization of centrifugal blood pumps. In this study, a screw centrifugal blood pump was used as the research object. In addition, pressure generation and the hemolysis level were optimized simultaneously using a coupled algorithm composed of random forest (RF) and multi-objective gray wolf optimization (MOGWO). After verifying the prediction accuracy of the algorithm, three optimized models were selected and compared with the baseline model in terms of pressure cloud, 2D streamline, SSS distribution, HI distribution, and vortex distribution. Finally, via a comprehensive evaluation, the optimized model was selected as the final optimization design, in which the pressure generation increased by 24% and the hemolysis value decreased by 48%.
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ISSN:2072-666X
2072-666X
DOI:10.3390/mi14020406