Hello all, Thank you for a very useful package. Me and my colleagues have two questions about partitioning for solving in parallel and implicit time integration. 1) Partitioning This question has as I understand it, been raised before ... The default partitioning of the grid when running in parallel is to divide the mesh into equal slices/slabs distributed on the different processor nodes when solving a 2D/3D problem. If assuming an equidistant grid (nx=ny=nz) This would imply that the maximum number of node that can be used to solve the problem is limited by the number nx, where the number of processor nodes must less or equal to nx. As we are using thermodynamics and kinetics coupled to an external package (TQ) and the calculation of these properties for each mesh point is computationally intense, we would like to use more processor nodes than is possible with this type of partitioning. Instead of "slicing" the mesh we would like to have a partitioning in a 3D equidistant simulation, so that volumes of the size "nx/N*ny/N*nz/N" where N^3 is the number of processor nodes, is used. Is this possible ?
2) Implicity When solving the Cahn-Hilliard problem using thermodynamics and kinetics provided from an external package we find that these are only evaluated at the beginning of the timestep implying the problem is solved explicitly. It should be noted we in this case don't have explicit expression for diffusivity as these are temperature and composition dependent. From my understanding the FiPy package should be able to solve this type of problem implicitly with higher degree of accuracy than using a fully explicit integration scheme. How do we ensure that all terms are solved implicitly? Thank you! Joakim _______________________________________________ fipy mailing list [email protected] http://www.ctcms.nist.gov/fipy [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
