Source code for imod.flow.slv

import jinja2

from imod.flow.pkgbase import Package

[docs]class PreconditionedConjugateGradientSolver(Package): """ The Preconditioned Conjugate Gradient Solver is used to solve the finite difference equations in each step of a MODFLOW stress period. Parameters ---------- max_iter: int is the maximum number of outer iterations - that is, calss to the solutions routine (MXITER). For a linear problem max_iter should be 1, unless more than 50 inner iterations are required, when max_iter could be as large as 10. A larger number (generally less than 100) is required for a nonlinear problem. inner_iter: int is the number of inner iterations (iter1). For nonlinear problems, inner_iter usually ranges from 10 to 30; a value of 30 will be sufficient for most linear problems. rclose: float is the residual criterion for convergence, in units of cubic length per time. The units for length and time are the same as established for all model data. When the maximum absolute value of the residual at all nodes during an iteration is less than or equal to RCLOSE, and the criterion for HCLOSE is also satisfied (see below), iteration stops. Default value: 100.0. **Nota bene**: this is aimed at regional modelling. For detailed studies (e.g. lab experiments) much smaller values can be required. Very general rule of thumb: should be less than 10% of smallest cell volume. hclose: float is the head change criterion for convergence, in units of length. When the maximum absolute value of head change from all nodes during an iteration is less than or equal to HCLOSE, and the criterion for RCLOSE is also satisfied (see above), iteration stops. Default value: 1.0e-4. **Nota bene**: This is aimed at regional modelling, ` for detailed studies (e.g. lab experiments) much smaller values can be required. relax: float, optional is the relaxation parameter used. Usually, RELAX = 1.0, but for some problems a value of 0.99, 0.98, or 0.97 will reduce the number of iterations required for convergence. Default value: 0.98. matrix_conditioning_method: int, optional the flag used to select the matrix conditioning method 1 is for Modified Incomplete Cholesky (for use on scalar computers) 2 is for Polynomial (for use on vector computers or to conserve computer memory) damp: float, optional the damping factor. It is typically set equal to one, which indicates no damping. A value less than 1 and greater than 0 causes damping. DAMP does not affect inner iterations; instead, it affects outer iterations. Default value: 1.0. damp_transient: float, optional the damping factor for transient stress periods. it is read only when `damp` is specified as a negative value. If damp_transient is not read, then the single damping factor, `damp`, is used for both transient and steady-state stress periods. printout_interval: int, optional is the printout interval for PCG. If equal to zero, it is changed to 999. The maximum head change (positive or negative) and residual change are printed for each iteration of a time step whenever the time step is an even multiple of printout_interval. This printout also occurs at the end of each stress period regardless of the value of printout_interval. print_convergence_info: int, optional a flag that controls printing of convergence information from the solver: 0 is for printing tables of maximum head change and residual each iteration 1 is for printing only the total number of iterations 2 is for no printing 3 is for printing only if convergence fails """ _pkg_id = "pcg" # TODO: update when all options are known _variable_order = [ "max_iter", "inner_iter", "hclose", "rclose", "relax", "matrix_conditioning_method", "damp", "damp_transient", "printout_interval", "print_convergence_info", ] _template_projectfile = jinja2.Template( "0001, ({{pkg_id}}), 1, Precondition Conjugate-Gradient\n" "{{max_iter}}, {{inner_iter}}, {{hclose}}, {{rclose}}, {{relax}}, " "{{matrix_conditioning_method}}, {{printout_interval}}, {{print_convergence_info}}, {{damp}}, {{damp_transient}}, " "{{IQERROR}}, {{QERROR}}\n" )
[docs] def __init__( self, max_iter=150, inner_iter=100, rclose=1.0, hclose=1.0e-4, relax=0.98, damp=1.0, damp_transient=1.0, matrix_conditioning_method=1, printout_interval=0, print_convergence_info=1, ): super().__init__() self.dataset["max_iter"] = max_iter self.dataset["inner_iter"] = inner_iter self.dataset["rclose"] = rclose self.dataset["hclose"] = hclose self.dataset["relax"] = relax self.dataset["matrix_conditioning_method"] = matrix_conditioning_method self.dataset["damp"] = damp self.dataset["damp_transient"] = damp_transient self.dataset["printout_interval"] = printout_interval self.dataset["print_convergence_info"] = print_convergence_info # TODO Check with Peter what these settings do # I now used the settings from the LHM here... self.dataset["IQERROR"] = 1 self.dataset["QERROR"] = 5.0
def _render_projectfile(self, **kwargs): d = {k: v.item() for k, v in self.dataset.items()} d["pkg_id"] = self._pkg_id return self._template_projectfile.render(**d)