import jinja2
import numpy as np
import scipy.ndimage
from imod.wq.pkgbase import Package
[docs]
class BasicTransport(Package):
"""
Handles basic tasks that are required by the entire transport model. Among
these tasks are definition of the problem, specification of the boundary and
initial conditions, determination of the stepsize, preparation of mass
balance information, and printout of the simulation results.
Parameters
----------
icbund: xr.DataArray of int
is an integer array specifying the boundary condition type (inactive,
constant-concentration, or active) for every model cell. For
multi-species simulation, ICBUND defines the boundary condition type
shared by all species. Note that different species are allowed to have
different constant-concentration conditions through an option in the
Source and Sink Mixing Package.
ICBUND=0, the cell is an inactive concentration cell for all species.
Note that no-flow or "dry" cells are automatically converted into
inactive concentration cells. Furthermore, active cells in terms of flow
can be treated as inactive concentration cells to minimize the area
needed for transport simulation, as long as the solute transport is
insignificant near those cells.
ICBUND<0, the cell is a constant-concentration cell for all species. The
starting concentration of each species remains the same at the cell
throughout the simulation. (To define different constantconcentration
conditions for different species at the same cell location, refer to the
Sink/Source Mixing Package.) Also note that unless explicitly defined as
a constant-concentration cell, a constant-head cell in the flow model is
not treated as a constantconcentration cell.
If ICBUND>0, the cell is an active (variable) concentration cell where
the concentration value will be calculated.
starting_concentration: float or xr.DataArray of floats
is the starting concentration (initial condition) at the beginning of
the simulation (unit: ML-3) (SCONC). For multispecies simulation, the
starting concentration must be specified for all species, one species at
a time.
porosity: float, optional
is the "effective" porosity of the porous medium in a single porosity
system (PRSITY).
Default value is 0.35.
n_species: int, optional
is the total number of chemical species included in the current
simulation (NCOMP). For single-species simulation, set n_species = 1.
Default value is 1.
inactive_concentration: float, optional
is the value for indicating an inactive concentration cell (ICBUND=0)
(CINACT). Even if it is not anticipated to have inactive cells in the
model, a value for inactive_concentration still must be submitted.
Default value is 1.0e30
minimum_active_thickness: float, optional
is the minimum saturated thickness in a cell (THKMIN), expressed as the
decimal fraction of the model layer thickness, below which the cell is
considered inactive.
Default value is 0.01 (i.e., 1% of the model layer thickness).
"""
_pkg_id = "btn"
_mapping = (("icbund", "icbund"), ("dz", "thickness"), ("prsity", "porosity"))
_template = jinja2.Template(
"[btn]\n"
" ncomp = {{n_species}}\n" # Number of components
" mcomp = {{n_species}}\n" # Number of mobile components
" thkmin = {{minimum_active_thickness}}\n"
" cinact = {{inactive_concentration}}\n"
" {%- for species, layerdict in starting_concentration.items() %}\n"
" {%- for layer, value in layerdict.items() %}\n"
" sconc_t{{species}}_l{{layer}} = {{value}}\n"
" {%- endfor -%}\n"
" {%- endfor -%}\n"
" {%- for name, dictname in mapping -%}\n"
" {%- for layer, value in dicts[dictname].items() %}\n"
" {{name}}_l{{layer}} = {{value}}\n"
" {%- endfor -%}\n"
" {%- endfor -%}\n"
)
[docs]
def __init__(
self,
icbund,
starting_concentration,
porosity=0.35,
n_species=1,
inactive_concentration=1.0e30,
minimum_active_thickness=0.01,
):
super().__init__()
self["icbund"] = icbund
self["starting_concentration"] = starting_concentration
self["porosity"] = porosity
self["n_species"] = n_species
self["inactive_concentration"] = inactive_concentration
self["minimum_active_thickness"] = minimum_active_thickness
def _render(self, directory, nlayer):
"""
Renders part of [btn] section that does not depend on time,
and can be inferred without checking the BoundaryConditions.
Parameters
----------
directory : str
thickness : xr.DataArray
Taken from BasicFlow
Returns
-------
rendered : str
"""
d = {}
dicts = {}
d["mapping"] = self._mapping
# Starting concentration also includes a species, and can't be written
# in the same way as the other variables; _T? in the runfile
if "species" in self.dataset["starting_concentration"].coords:
starting_concentration = {}
for i, species in enumerate(
self.dataset["starting_concentration"]["species"].values
):
da = self.dataset["starting_concentration"].sel(species=species)
starting_concentration[i + 1] = self._compose_values_layer(
"starting_concentration", directory, nlayer=nlayer, da=da
)
d["starting_concentration"] = starting_concentration
else:
d["starting_concentration"] = {
1: self._compose_values_layer(
"starting_concentration", directory, nlayer=nlayer
)
}
# Collect which entries are complex (multi-dim)
data_vars = [t[1] for t in self._mapping]
for varname in self.dataset.data_vars.keys():
if varname == "starting_concentration":
continue # skip it, as mentioned above
if varname in data_vars: # multi-dim entry
dicts[varname] = self._compose_values_layer(
varname, directory, nlayer=nlayer
)
else: # simple entry, just get the scalar value
d[varname] = self.dataset[varname].values
# Add these from the outside, thickness from BasicFlow
# layer_type from LayerPropertyFlow
dicts["thickness"] = self._compose_values_layer(
"thickness", directory, nlayer=nlayer, da=self.dataset.thickness
)
d["dicts"] = dicts
return self._template.render(d)
def _pkgcheck(self, ibound=None):
to_check = [
"starting_concentration",
"porosity",
"n_species",
"minimum_active_thickness",
]
self._check_positive(to_check)
active_cells = self.dataset["icbund"] != 0
if (active_cells & np.isnan(self.dataset["starting_concentration"])).any():
raise ValueError(
f"Active cells in icbund may not have a nan value in starting_concentration in {self}"
)
_, nlabels = scipy.ndimage.label(active_cells.values)
# if nlabels > 1:
# raise ValueError(
# f"{nlabels} disconnected model domain detected in the icbund in {self}"
# )
