Numerical simulation of red blood cells flowing in a blood analyzer

Abstract : The aim of this thesis is to improve the understanding of the phenomena involved in the measurement performed in a blood analyzer, namely the counting and sizing of red blood cells based on the Coulter effect. Numerical simulations are performed to predict the dynamics of red blood cells in the measurement regions, and to reproduce the associated electrical measurement used to count and size the cells. These numerical simulations are performed in industrial configurations using a numerical tool developed at IMAG, the YALES2BIO solver. Using the Front-Tracking Immersed Boundary Method, a deformable particle model for the red blood cell is introduced which takes the viscosity contrast as well as the mechanical effects of the curvature and elasticity on the membrane into account. The solver is validated against several test cases spreading over a large range of regimes and physical effects.The velocity field in the blood analyzer geometry is found to consist of an intense axial velocity gradient in the direction of the flow, resulting in a extensional flow at the micro-orifice, where the measurement is performed. The dynamics of the red blood cells is studied with numerical simulations with different initial conditions, such as its position or orientation. They are found to reorient along the main axis of the blood analyzer in all cases. In order to understand the phenomenon, analytical models are adapted to the case of extensional flows and are found to reproduce the observed trends.This thesis also presents the reproduction of the electrical measurement used to count red blood cells and measure their volume distribution. Numerous dynamics simulations are performed and used to generate the electrical pulse corresponding to the passage of a red blood cell inside the micro-orifice. The resulting electrical pulse amplitudes are used to characterize the electrical response depending on the initial parameters of the simulation by means of a statistical approach. A Monte-Carlo algorithm helps quantifying the errors on the measurement of cell depending on its orientation and position inside the micro-orifice. This allows the generation of a measured volume distribution of a well defined red blood cell population and the characterization of the associated measurement errors.
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Etienne Gibaud. Numerical simulation of red blood cells flowing in a blood analyzer. Hematology. Université Montpellier, 2015. English. ⟨NNT : 2015MONTS135⟩. ⟨tel-01981003⟩

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