Hello All, I have two questions that I'm hoping you may be able to help me with.
Firstly, the FiPy code I'm working with sweeps for four inter-dependent CellVariables / PDEs in a loop until their residuals falls below specified tolerances. After profiling the code, it's clear that this sweeping process accounts for ~95% of the time spent solving in a given timestep. Consequently, advancing in time is painfully slow. Assuming that the CellVariable tolerances are fixed at their largest allowable values, what would be the best strategy for me to take to reduce the time spent sweeping these variables? Secondly, some (but not all) of the boundary conditions for these four PDEs need to be updated between sweeping one CellVariable and the next. Notably, this is within a single timestep because the boundary conditions are functions of the CellVariables being swept. What is the recommended approach for updating the values of these boundary conditions, and why? At present, the following is the approach used: define the boundary condition only within the loop using x.constrain(value, where=some_boundary). i.e. the boundary condition is re-defined with new variables at every pass through the loop. However, I suspect that this approach may be incorrect, and that a better approach may be to define the boundary conditions only once, outside of the loop, and to update them by employing the .value approach for CellVariables. I am at a loss to understand why this approach may be better, though. With best regards, - Ian
_______________________________________________ fipy mailing list fipy@nist.gov http://www.ctcms.nist.gov/fipy [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ]
