Moving-grid methods

Paul Zegeling

What are moving-grid methods?

Moving-grid methods are solution-adaptive methods for time-dependent partial differential equations (PDEs). These methods, also characterized by the terms r-refinement (*), continuously deforming grids, or dynamically moving grids, move the spatial grid continuously in time while the discretization of the PDE and the grid selection procedure are intrinsically coupled.

(*) h-refinement methods ('local refinement') adapt the grid only at discrete time levels, whereas p-refinement methods (mostly of finite-element type) increase or decrease the order of approximation where, when and if necessary. In advanced applications also hybrid methods exist: h-p-, h-r-, or r-p-refinement.
Moving-grid methods use a fixed number of spatial grid points, without need of interpolation, and let them move with "whatever fronts are present". Using these methods, if properly applied, can save up to a factor of 10 (in 1D) or 100 (in 2D) spatial grid points in the case of steep moving layers stemming from a convection-diffusion or reaction-diffusion system.

Comparison of a fixed grid with a moving grid

The following four pictures illustrate the difference between fixed grid and moving grid methods for the one-dimensional Burger's equation.

[4 pictures, 21 kB in total]

The following movie shows a uniform non-moving central-difference scheme for a two-dimensional Burgers' type equation :

du/dt = 0.001 Laplace u - u du/dx - u du/dy

[mpeg-movie, 351 kB]

Grid degeneration

An intrinsic difficulty of topological nature exists: so-called `grid distortion' or `grid degeneration'. In terms of transformation of variables (non-uniform in (x,y,t) -> uniform (\xi,\eta,\tau)) this means that for some instances the Jacobian may become zero or almost zero.

The following pictures show three possible cases of grid degeneration.

[3 pictures, 27 kB in total]

Applications

An interesting application of a moving-grid method in 1D can be found in a tidally-averaged advection-dispersion model. Application of the same method to a `lava-dome' model can be found in lava-dome model. Recent work by the author dealing with moving grids concerns the application to the Gray-Scott reaction-diffusion model, which describes two irreversible chemical reactions. The PDE-system reads :

U_t = D_u LAPLACE U - U V^2 + F(1-U) & V_t = D_v LAPLACE V + U V^2 - (F+k)V

where k is the dimensionless rate constant of the second reaction and F is the dimensionless feed rate. It is known from experiments that many different complex patterns (regular, stationary, periodic, chaotic) can be found for different choices of the parameters.

Even in one space dimension interesting solution behaviour may occur for which moving-grid methods could be benificial.

Self replicating patterns in 1D

For the 1D-case a moving-finite-difference method based on equidistribution (with spatial and temporal smoothing) is used to detect the splitting of the pulses.

Self-replicating patterns recognized by moving grids :

[gif, 76 kB]

Irregular solution behaviour created by changing the parameters :

[gif, 72 kB]

Self replicating patterns in 2D

For the 2D-case a moving-finite-element method based on minimizing the PDE-residual (with regularization) gives:

The following movie shows the U-component of another pattern in two dimensions (a "moving vulcano"):

[mpeg-movie, 1.5 MB]

Moving Finite Differences in 2D

Below you find some results obtained by a moving finite difference method in two space dimensions, which is based on smoothed equidistribution. It is applied to a 2D Burgers' equation.

> Moving-finite-differences (N=21) for Burgers' equation at t=0, t=0.1 :

Many references on this subject can be found [HERE]

Other interesting pages:

Gradient-weighted moving-finite-elements: Neil Carlson, Purdue University (also 3D!).

Moving-finite-elements: Mike Baines, University of Reading.

Moving-finite-volumes: John Mackenzie, Strathclyde University.

Moving-mesh-PDEs: Weizhang Huang, University of Kansas; and Bob Russell, Simon Fraser University.

The method-of-lines and adaptive grids: Bill Schiesser, Lehigh University.

Moving grids with the deformation method: Guojun Liao, Univ. of Texas at Arlington.

Application of moving grids to PDE models with Boussinesq convection (also movies): Hector Ceniceros, Univ. of California (Santa Barbara).

Moving grids and harmonic maps: Tang Tao, Hong Kong Baptist University.

Books on adaptive grids and grid generation