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Adaptivity with moving grids

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<strong>Adaptivity</strong> <strong>with</strong> <strong>moving</strong> <strong>grids</strong> 79<br />

properties of the solution such as scaling structures (Budd et al. 1996, Budd<br />

and Williams 2006, Baines et al. 2006). Alternatively, the <strong>moving</strong> mesh<br />

equations and (3.49) can be solved alternately (Huang 2007, Huang and<br />

Russell 1999, Ceniceros and Hou 2001). This reduces the stiffness problems<br />

but can lead to a lag in the mesh movement.<br />

Alternatively, a rezoning method can be used (Tang 2005). In such methods,<br />

the MMPDEs are solved to advance the mesh by one time step. The<br />

current solution u is then interpolated onto this new mesh in the physical<br />

domain, and the original PDE (3.48) solved on the new mesh in this domain<br />

(often using a finite element or finite volume method). The advantage of<br />

this method is that standard software can be used to solve the PDE, but<br />

at the expense of an interpolation step. We now describe these methods in<br />

more detail.<br />

3.4.1. Simultaneous solution in the computational domain<br />

We first briefly detail the implementation of the simultaneous solution<br />

method solving the MMPDEs and (3.49) by using finite differences in the<br />

computational domain. To do this we associate each mesh point in the fixed<br />

computational mesh <strong>with</strong> a solution point U i,j (t). To discretize (3.49) we<br />

transform the derivatives in the physical domain to ones in the computational<br />

domain. For example, in two dimensions, if we have general transformation<br />

F <strong>with</strong> Jacobian J then the derivatives of u can be expressed in<br />

terms of the computational variables in the following manner:<br />

u x = 1 J<br />

(<br />

yη u ξ − y ξ u η<br />

)<br />

, (3.50)<br />

u y = 1 ( )<br />

−xη u ξ + x ξ u η ,<br />

J<br />

u xx = 1 ( (<br />

yη J −1 )<br />

y η u ξ<br />

J<br />

ξ − y (<br />

η J −1 )<br />

y ξ u η ξ − y (<br />

ξ J −1 )<br />

y η u ξ η + y (<br />

ξ J −1 )<br />

y ξ u η η)<br />

,<br />

u yy = 1 ( (x η J −1 )<br />

x η u ξ<br />

J<br />

ξ − x (<br />

η J −1 )<br />

x ξ u η ξ − x (<br />

ξ J −1 )<br />

x η u ξ η + x (<br />

ξ J −1 ) )<br />

x ξ u η .<br />

η<br />

As the computational domain has a uniform mesh, and the Jacobian J may<br />

be determined directly from the mesh mapping F , it follows that (3.50) can<br />

be discretized to high accuracy in space using a standard finite difference or<br />

finite element discretization. The solution u is then determined by solving<br />

(3.46) and (3.49) together using an appropriate ODE solver such as SDIRK<br />

or a predictor–corrector method. Examples of the use of this method are<br />

given in Section 5.<br />

3.4.2. Alternating solution in the computational domain<br />

The alternating solution method is often used in higher-dimensional calculations<br />

to alternatively solve the system and to move the mesh. This method

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