Data informed, predictive simulations of blood microfluidics
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Date
2020
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Doctoral Thesis
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Abstract
Blood transport is a fundamental process in the human body and has attracted numerous studies throughout the years, due to its direct physiological and medical significance. The transport properties and rheology of blood are governed by the hydrodynamic interactions between neighboring Red Blood Cells (RBCs) and the surrounding plasma. Understanding the complex interplay among RBCs can provide insights to the origin of certain physiological observations that have perplexed the scientific community for decades, such as the shear-thinning behavior of blood, and potentially assist in the design of microfluidic devices and the improvement of diagnostic measures for pathogenic diseases.
The quest for information acquisition regarding the dynamics of RBCs in blood flows has led to remarkable technological advancements in the field of experimental microfluidics. At the same time, mathematical models have been developed to describe the physics of flowing RBCs, envisioning to complement the information that can be extracted from experiments, with their predictions, however, being highly dependent on the assumed RBC viscoelastic properties. In this thesis, the importance of rigorous model calibration is identified by quantifying the variability in blood flow predictions, arising from differences in RBC viscoelastic properties. Hierarchical Bayesian inference is then employed to perform a data-driven calibration of RBC models, based on multiple experimental data-sets available in the literature, obtained under different experimental conditions. Subsequent studies deal with the characterization of RBC dynamics in capillary vessels and in simple shear flows, with the goal of analyzing the RBC behavior under physiological blood flow conditions.
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Examiner : Koumoutsakos, Petros
Examiner : Papadimitriou, Costas
Examiner : Litvinov, Sergey
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ETH Zurich
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03499 - Koumoutsakos, Petros (ehemalig) / Koumoutsakos, Petros (former)