Adaptive mesh refinement

This article is about the use of adaptive meshing in numerical analysis. See Subdivision surface for the use of adaptive techniques in Computer Graphics modelling.

In numerical analysis, adaptive mesh refinement is a method of adaptive meshing. Central to any Eulerian method is the manner in which it discretizes the continuous domain of interest into a grid of many individual elements. This grid may be static, established once and for all at the beginning of the computation, or it may be dynamic, tracking the features of the result as the computation progresses. If the computation has features which one wants to track which are much smaller than the overall scale of the problem, and which move in time, then one must either include many more static grids to cover the region of interest, or adopt a dynamic scheme.^[1]

The advantages of a dynamic gridding scheme are:

1. Increased computational savings over a static grid approach.
2. Increased storage savings over a static grid approach.
3. Complete control of grid resolution, compared to the fixed resolution of a static grid approach, or the Lagrangian-based adaptivity of smoothed particle hydrodynamics.

Introduction to adaptive mesh refinement

The image above shows the grid structure of an AMR calculation of a shock impacting an inclined slope. Each of the boxes is a grid; the more boxes it is nested within, the higher the level of refinements. As the image shows, the algorithm uses high resolution grids only at the physical locations and times where they are required.

In a series of papers, Marsha Berger, Joseph Oliger, and Phillip Colella developed an algorithm for dynamic gridding called local adaptive mesh refinement. The algorithm begins with the entire computational domain covered with a coarsely resolved base-level regular Cartesian grid. As the calculation progresses, individual grid cells are tagged for refinement, using a criterion that can either be user-supplied (for example mass per cell remains constant, hence higher density regions are more highly resolved) or based on Richardson extrapolation.

All tagged cells are then refined, meaning that a finer grid is overlaid on the coarse one. After refinement, individual grid patches on a single fixed level of refinement are passed off to an integrator which advances those cells in time. Finally, a correction procedure is implemented to correct the transfer along coarse-fine grid interfaces, to ensure that the amount of any conserved quantity leaving one cell exactly balances the amount entering the bordering cell. If at some point the level of refinement in a cell is greater than required, the high resolution grid may be removed and replaced with a coarser grid.

This allows the user to solve problems that are completely intractable on a uniform grid; for example, astrophysicists have used AMR to model a collapsing giant molecular cloud core down to an effective resolution of 131,072 cells per initial cloud radius, corresponding to a resolution of 10¹⁵ cells on a uniform grid.^[2]

References

Berger, M. J.; Colella, P. (1989). "Local adaptive mesh refinement for shock hydrodynamics". J. Comput. Phys. (Elsevier) 82: 64–84.

1. ^ Adaptive Mesh Refinement - Theory And Applications. Springer. 2005. ISBN 3540211470. 
2. ^ Klein, Richard (1999). "Star formation with 3-D adaptive mesh refinement: the collapse and fragmentation of molecular clouds". Journal of Computational and Applied Mathematics 109 (1-2): 123–152. doi:10.1016/S0377-0427(99)00156-9. http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TYH-3Y0R65K-V&_user=483702&_coverDate=09/30/1999&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1643511112&_rerunOrigin=scholar.google&_acct=C000022720&_version=1&_urlVersion=0&_userid=483702&md5=577df4203e5f672ac4b9e8acad8db5db&searchtype=a. Retrieved February 16, 2011. 

See also

• Adaptive stepsize
• Cactus Framework
• Multigrid method
• Silo (library)