Biomedical Engineering Reference
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deformations of each individual red blood cell, and the hydrodynamic interactions
between the red blood cells and the surrounding plasma. Software named MoBo
(for “Moving Boundaries”) was developed and applied on a supercomputer cluster,
namely the Teragrid's Lincoln cluster that had 200,000 computer processor cores on
the Oak Ridge National Laboratory's Jaguar PF system.
9.2.3
Blood Rheology in Large Arteries Using Lattice Boltzman
Simulating 3D deformable particle suspensions using the Lattice Boltzmann meth-
od with discrete external boundary force is an alternative approach being used by a
number of researchers (Melchionna et al. 2013; Reasor et al. 2012; van Wyk et al.
2013; Wu and Aidun 2010). When studying disease development in arteries, it is
important to understand local variations in blood rheology. Blood flow in large
arteries is often assumed to behave as a homogeneous fluid, an assumption that is
not entirely correct. The local viscosity changes, concentration of red blood cells,
and the rate of shear strongly influences the wall shear stress and its gradients. The
red blood cell flow behaviour is also influenced by the flow environment geometry.
Experimentally, rheological properties across a tube cross-section are difficult to
measure if non-invasive techniques are used. The Lattice Boltzmann method can
be used to model blood as a particle suspension of red blood cells. The multiphase
mixture is made up of plasma (55 % volume fraction), red blood cells (45 % volume
fraction), white blood cells (< 1 % volume fraction), and platelets (< 1 % volume
fraction). The plasma is accurately modelled as a Newtonian fluid, with a tempera-
ture dependent viscosity. The whole blood rheology depends on the interaction be-
tween red blood cells and the plasma (e.g. dependency on hematocrit, shear rate, red
blood cell deformability, and vessel geometry). Viscosity models are usually based
on steady state rheometer measurements. The particles are represented in Lagrang-
ian coordinates and the fluid on a regular Eulerian grid. Particle-particle interactions
are only resolved down to the grid spacing, where the idea is to resolve the far-field
interactions and use a subgrid model at short ranges. Results from van Wyke et al.
(2013) are given in Fig.
9.3
.
9.3
Medical Imaging for Flow Validation and Analysis
9.3.1
Imaging for Flow Validation
The recent developments in the fields of CT and MRI mapping systems, velocity-
encoded measurements, computational modelling and simulation, experimental
testing instruments, as well as manufacturing processes are giving new dimensions
to our understanding of the pathology and the impact of cardiovascular disease.
Experimental and clinical verification complements the existing computational re-
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