AMRClaw Description and Detailed Contents¶
The AMRClaw version of Clawpack provides Adaptive Mesh Refinement (AMR) capabilities in 2 and 3 space dimensions. (The two-dimensional code can also be used for 1-dimensional problems, see AMRClaw for 1d problems.)
Block-structured AMR is implemented, in which rectangular patches of the grid at level L are refined to level L+1. See Specifying AMRClaw run-time parameters in setrun.py for a list of the input parameters that can be specified to help control how refinement is done. The general algorithms are described in [BergerLeVeque98].
See ClawPlotItem for a list of 2d plot types that can be used to create a setplot function to control plotting of two-dimensional results. Some of the attribute names start with the string amr_, indicating that a list of different values can be specified for each AMR level. See Plotting with Visclaw and Using setplot.py to specify the desired plots for more about plotting.
Python plotting tools for 3d are still under development. For now, the Matlab tools from Clawpack 4.3 can still be used, see Plotting using Matlab.
- AMRClaw for 1d problems
- Specifying AMRClaw run-time parameters in setrun.py
- Sample setrun.py module for AMRClaw
- Adaptive mesh refinement (AMR) algorithms
- AMR refinement criteria
- Specifying flagregions for adaptive refinement
- Ruled Rectangles
- Guiding AMR with adjoint flagging
- Doxygen documentation of AMRClaw
- AMRClaw Flowcharts