efficient parallel computation to simulate blood flow - CiteSeerX
efficient parallel computation to simulate blood flow - CiteSeerX
efficient parallel computation to simulate blood flow - CiteSeerX
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The NS equations simplify then in<strong>to</strong> the S<strong>to</strong>kes equation in the neighborhood of<br />
the inlet and outlet of the fluid domain Ω f . To avoid reflection of acoustic waves at<br />
the outlet one may then use transparent boundary conditions [19].<br />
On the horizontal wall we assume either the periodicity of all variables, or we<br />
impose the <strong>flow</strong> speed <strong>to</strong> be U w .<br />
Further improvement in the grid discretization implementation may use a high<br />
order interpolation scheme for the convective term that introduces less dissipation<br />
and a mesh refinement in the neighborhood of the interface Sw.<br />
f<br />
We have all the information <strong>to</strong> solve the NS equations. The main goal of this<br />
project is <strong>to</strong> get the fastest results. A short <strong>computation</strong> time <strong>to</strong> obtain a solution<br />
of the NS equation requires an <strong>efficient</strong> solver.<br />
3.3 Aitken Schwarz 3D<br />
For solving the linear system, our solver uses also the Aitken-Schwarz Domain decomposition<br />
technique presented in Chapter 2 for a two dimensional case. The main<br />
difference between the two and three dimensional simulation is that the Fourier decomposition<br />
and the subdomain solver solve only a tridiagonal problem.<br />
The domain Ω = (0, Lx) × (0, Ly) × (0, Lz) is decomposed in<strong>to</strong> q overlapping<br />
strips Ω i = (X i l , Xi r ) × (0, Ly) × (0, Lz) with X2 l < X 1 r < X3 l < X 2 r , ..., Xq l<br />
< X q−1<br />
r<br />
as<br />
described in Figure 3.4<br />
The pressure unknown is developed in<strong>to</strong> the sine expansion in<strong>to</strong> the physical space<br />
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