4.5 Article

Hemolysis estimation in turbulent flow for the FDA critical path initiative centrifugal blood pump

期刊

BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
卷 20, 期 5, 页码 1709-1722

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10237-021-01471-3

关键词

Red blood cell trauma; Hemolysis; FDA; Turbulence; Kolmogorov length scale; Erythrocyte; Artificial organs

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This study examined the effects of turbulence on red blood cell damage in a centrifugal blood pump, which was identified as a case study by the FDA. The model used for predicting hemolysis in turbulent flow environment showed good prediction for the 2500-rpm pump, but overpredicted for the 3500-rpm pump. Refinement of the model and empirical constants with better experimental data are needed for further improvement.
Hemolysis in medical devices and implants has been a primary concern over the past fifty years. Turbulent flow in particular can cause cell trauma and hemolysis in such devices. In this work, the effects of turbulence on red blood cell (RBC) damage are examined by simulating the flow field through a centrifugal blood pump that has been identified as a case study through the critical path initiative of the US Food and Drug Administration (FDA). In this study, a new model was employed to predict hemolysis in the turbulent flow environment in the pump selected for the FDA critical path initiative. The operating conditions for a centrifugal blood pump were specified by the FDA for rotational speeds of 2500 and 3500 rpm. The model is based on the analysis of the smaller eddies within the turbulent flow field, since it is assumed that turbulent flow eddies with sizes comparable to the dimensions of the RBCs lead to cell trauma. The Kolmogorov length scale of the velocity field is used to identify such small eddies. Using model parameters obtained in prior work through comparisons to capillary and jet flow, it is found that hemolysis for the 2500-rpm pump was predicted well, while hemolysis for the 3500-rpm pump was overpredicted. Results indicate refinement of the model and empirical constants with better experimental data is needed.

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