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56<br />

2. MAPPING THE NUMERICAL SIMULATIONS OF PARTIAL<br />

DIFFERENTIAL EQUATIONS<br />

(n+1;m+1)<br />

x<br />

x<br />

n 2<br />

x<br />

x<br />

x x x<br />

(i;j)<br />

n 1<br />

x<br />

x<br />

x<br />

n 3<br />

n 4<br />

x<br />

x<br />

(1;1)<br />

Figure 2.12: The computational domain<br />

2.3.2.3 The First-order Scheme<br />

The simplest algorithm we consider is first-order both in space <strong>and</strong> time [56] [57].<br />

The application of the finite volume discretization method leads to the following<br />

semi-discrete form of governing equations (2.6)<br />

dU i,j<br />

dt<br />

= − 1 ∑<br />

F f · n f , (2.9)<br />

V i,j<br />

where the summation is meant for all the four faces of cell (i,j), F f is the flux<br />

tensor evaluated at face f, <strong>and</strong> n f is the outward pointing normal vector of face<br />

f scaled by the length of the face. Let us consider face f in a coordinate frame<br />

attached to the face, such that its x-axes is normal to f. Face f separates cell<br />

L (left) <strong>and</strong> cell R (right). In this case the F f · n f scalar product equals to<br />

the x-component of F(F x ) multiplied by the length of the face (or area in 3D<br />

case). In order to stabilize the solution procedure, artificial dissipation has to be<br />

introduced into the scheme. Following the st<strong>and</strong>ard procedure, this is achieved<br />

by replacing the physical flux tensor by the numerical flux function F N containing<br />

f

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