def ext_theis_tpl_3d(
time,
rad,
storage,
cond_gmean,
len_scale,
hurst,
var=None,
c=1.0,
anis=1,
lat_ext=1.0,
rate=-1e-4,
r_well=0.0,
r_bound=np.inf,
h_bound=0.0,
K_well="KH",
prop=1.6,
far_err=0.01,
struc_grid=True,
parts=30,
lap_kwargs=None,
):
"""
The extended Theis solution for truncated power-law fields in 3D.
The extended Theis solution for transient flow under
a pumping condition in a confined aquifer with anisotropy in 3D.
The type curve is describing the effective drawdown
in a 3-dimensional statistical framework,
where the conductivity distribution is
following a log-normal distribution with a truncated power-law
correlation function build on superposition of gaussian modes.
Parameters
----------
time : :class:`numpy.ndarray`
Array with all time-points where the function should be evaluated
rad : :class:`numpy.ndarray`
Array with all radii where the function should be evaluated
storage : :class:`float`
Storage of the aquifer.
cond_gmean : :class:`float`
Geometric-mean conductivity.
len_scale : :class:`float`
Corralation-length of log-conductivity.
hurst: :class:`float`
Hurst coefficient of the TPL model. Should be in (0, 1).
var : :class:`float`
Variance of the log-conductivity.
If var is given, c will be calculated accordingly.
Default: :any:`None`
c : :class:`float`, optional
Intensity of variation in the TPL model.
Is overwritten if var is given.
Default: ``1.0``
anis : :class:`float`, optional
Anisotropy-ratio of the vertical and horizontal corralation-lengths.
Default: 1.0
lat_ext : :class:`float`, optional
Lateral extend of the aquifer (thickness).
Default: ``1.0``
rate : :class:`float`, optional
Pumpingrate at the well. Default: -1e-4
r_well : :class:`float`, optional
Radius of the pumping-well. Default: ``0.0``
r_bound : :class:`float`, optional
Radius of the outer boundary of the aquifer. Default: ``np.inf``
h_bound : :class:`float`, optional
Reference head at the outer boundary as well as initial condition.
Default: ``0.0``
K_well : :class:`float`, optional
Explicit conductivity value at the well. One can choose between the
harmonic mean (``"KH"``), the arithmetic mean (``"KA"``) or an
arbitrary float value. Default: ``"KH"``
prop: :class:`float`, optional
Proportionality factor used within the upscaling procedure.
Default: ``1.6``
far_err : :class:`float`, optional
Relative error for the farfield transmissivity for calculating the
cutoff-point of the solution. Default: ``0.01``
struc_grid : :class:`bool`, optional
If this is set to ``False``, the `rad` and `time` array will be merged
and interpreted as single, r-t points. In this case they need to have
the same shapes. Otherwise a structured r-t grid is created.
Default: ``True``
parts : :class:`int`, optional
Since the solution is calculated by setting the transmissivity to local
constant values, one needs to specify the number of partitions of the
transmissivity. Default: ``30``
lap_kwargs : :class:`dict` or :any:`None` optional
Dictionary for :any:`get_lap_inv` containing `method` and
`method_dict`. The default is equivalent to
``lap_kwargs = {"method": "stehfest", "method_dict": None}``.
Default: :any:`None`
Returns
-------
head : :class:`numpy.ndarray`
Array with all heads at the given radii and time-points.
Notes
-----
If you want to use cartesian coordiantes, just use the formula
``r = sqrt(x**2 + y**2)``
"""
# check the input
if r_well < 0.0:
raise ValueError("The wellradius needs to be >= 0")
if not r_bound > r_well:
raise ValueError("The upper boundary needs to be > well radius")
if not storage > 0.0:
raise ValueError("The storage needs to be positive.")
if not cond_gmean > 0.0:
raise ValueError("The gmean conductivity needs to be positive.")
if not len_scale > 0.0:
raise ValueError("The correlationlength needs to be positive.")
if not 0 < hurst < 1:
raise ValueError("Hurst coefficient needs to be in (0,1)")
if var is not None and var < 0.0:
raise ValueError("The variance needs to be positive.")
if var is None and not c > 0.0:
raise ValueError("The intensity of variation needs to be positive.")
if K_well != "KA" and K_well != "KH" and not isinstance(K_well, float):
raise ValueError(
"The well-conductivity should be given as float or 'KA' resp 'KH'")
if isinstance(K_well, float) and not K_well > 0.0:
raise ValueError("The well-conductivity needs to be positive.")
if not prop > 0.0:
raise ValueError("The proportionality factor needs to be positive.")
if parts <= 1:
raise ValueError("The numbor of partitions needs to be at least 2")
if not 0.0 < far_err < 1.0:
raise ValueError(
"The relative error of Conductivity needs to be within (0,1)")
# genearte rlast from a given relativ-error to farfield-conductivity
r_last = TPL_CG_error(far_err, cond_gmean, len_scale, hurst, var, c, anis,
3, K_well, prop)
# generate the partition points
if r_last > r_well:
R_part = specialrange_cut(r_well, r_bound, parts + 1, r_last)
else:
R_part = np.array([r_well, r_bound])
# calculate the harmonic mean conductivity values within each partition
K_part = annular_hmean(
TPL_CG,
R_part,
ann_dim=2,
cond_gmean=cond_gmean,
len_scale=len_scale,
hurst=hurst,
var=var,
c=c,
anis=anis,
dim=3,
K_well=K_well,
prop=prop,
)
K_well = TPL_CG(r_well, cond_gmean, len_scale, hurst, var, c, anis, 3,
K_well, prop)
return ext_grf(
time=time,
rad=rad,
S_part=np.full_like(K_part, storage),
K_part=K_part,
R_part=R_part,
dim=2,
lat_ext=lat_ext,
rate=rate,
h_bound=h_bound,
K_well=K_well,
struc_grid=struc_grid,
lap_kwargs=lap_kwargs,
)
def neuman2004(
time,
rad,
storage,
trans_gmean,
var,
len_scale,
rate=-1e-4,
r_well=0.0,
r_bound=np.inf,
h_bound=0.0,
struc_grid=True,
parts=30,
lap_kwargs=None,
):
"""
The transient solution for the apparent transmissivity from [Neuman2004].
This solution is build on the apparent transmissivity from Neuman 2004,
which represents a mean drawdown in an ensemble of pumping tests in
heterogeneous transmissivity fields following an exponential covariance.
Parameters
----------
time : :class:`numpy.ndarray`
Array with all time-points where the function should be evaluated.
rad : :class:`numpy.ndarray`
Array with all radii where the function should be evaluated.
storage : :class:`float`
Storage of the aquifer.
trans_gmean : :class:`float`
Geometric-mean transmissivity.
var : :class:`float`
Variance of log-transmissivity.
len_scale : :class:`float`
Correlation-length of log-transmissivity.
rate : :class:`float`, optional
Pumpingrate at the well. Default: -1e-4
r_well : :class:`float`, optional
Radius of the pumping-well. Default: ``0.0``
r_bound : :class:`float`, optional
Radius of the outer boundary of the aquifer. Default: ``np.inf``
h_bound : :class:`float`, optional
Reference head at the outer boundary as well as initial condition.
Default: ``0.0``
struc_grid : :class:`bool`, optional
If this is set to ``False``, the `rad` and `time` array will be merged
and interpreted as single, r-t points. In this case they need to have
the same shapes. Otherwise a structured r-t grid is created.
Default: ``True``
parts : :class:`int`, optional
Since the solution is calculated by setting the transmissivity to local
constant values, one needs to specify the number of partitions of the
transmissivity. Default: ``30``
lap_kwargs : :class:`dict` or :any:`None` optional
Dictionary for :any:`get_lap_inv` containing `method` and
`method_dict`. The default is equivalent to
``lap_kwargs = {"method": "stehfest", "method_dict": None}``.
Default: :any:`None`
Returns
-------
head : :class:`numpy.ndarray`
Array with all heads at the given radii and time-points.
References
----------
.. [Neuman2004] Neuman, Shlomo P., Alberto Guadagnini, and Monica Riva.
''Type-curve estimation of statistical heterogeneity.''
Water resources research 40.4, 2004
"""
# check the input
if r_well < 0.0:
raise ValueError("The wellradius needs to be >= 0")
if not r_bound > r_well:
raise ValueError("The upper boundary needs to be > well radius")
if not storage > 0.0:
raise ValueError("The Storage needs to be positive.")
if not trans_gmean > 0.0:
raise ValueError("The Transmissivity needs to be positive.")
if var < 0.0:
raise ValueError("The variance needs to be positive.")
if not len_scale > 0.0:
raise ValueError("The correlationlength needs to be positive.")
if parts <= 1:
raise ValueError("The numbor of partitions needs to be at least 2")
# genearte rlast from a given relativ-error to farfield-transmissivity
r_last = 2 * len_scale
# generate the partition points
if r_last > r_well:
R_part = specialrange_cut(r_well, r_bound, parts + 1, r_last)
else:
R_part = np.array([r_well, r_bound])
# calculate the harmonic mean transmissivity values within each partition
T_part = annular_hmean(
neuman2004_trans,
R_part,
trans_gmean=trans_gmean,
var=var,
len_scale=len_scale,
)
T_well = neuman2004_trans(r_well, trans_gmean, var, len_scale)
return ext_grf(
time=time,
rad=rad,
S_part=np.full_like(T_part, storage),
K_part=T_part,
R_part=R_part,
dim=2,
lat_ext=1,
rate=rate,
h_bound=h_bound,
K_well=T_well,
struc_grid=struc_grid,
lap_kwargs=lap_kwargs,
)
def ext_theis_2d(
time,
rad,
storage,
trans_gmean,
var,
len_scale,
rate=-1e-4,
r_well=0.0,
r_bound=np.inf,
h_bound=0.0,
T_well=None,
prop=1.6,
struc_grid=True,
far_err=0.01,
parts=30,
lap_kwargs=None,
):
"""
The extended Theis solution in 2D.
The extended Theis solution for transient flow under
a pumping condition in a confined aquifer.
The type curve is describing the effective drawdown
in a 2D statistical framework, where the transmissivity distribution is
following a log-normal distribution with a gaussian correlation function.
Parameters
----------
time : :class:`numpy.ndarray`
Array with all time-points where the function should be evaluated
rad : :class:`numpy.ndarray`
Array with all radii where the function should be evaluated
storage : :class:`float`
Storage of the aquifer.
trans_gmean : :class:`float`
Geometric-mean transmissivity.
var : :class:`float`
Variance of log-transmissivity.
len_scale : :class:`float`
Correlation-length of log-transmissivity.
rate : :class:`float`, optional
Pumpingrate at the well. Default: -1e-4
r_well : :class:`float`, optional
Radius of the pumping-well. Default: ``0.0``
r_bound : :class:`float`, optional
Radius of the outer boundary of the aquifer. Default: ``np.inf``
h_bound : :class:`float`, optional
Reference head at the outer boundary as well as initial condition.
Default: ``0.0``
T_well : :class:`float`, optional
Explicit transmissivity value at the well. Harmonic mean by default.
prop: :class:`float`, optional
Proportionality factor used within the upscaling procedure.
Default: ``1.6``
far_err : :class:`float`, optional
Relative error for the farfield transmissivity for calculating the
cutoff-point of the solution. Default: ``0.01``
struc_grid : :class:`bool`, optional
If this is set to ``False``, the `rad` and `time` array will be merged
and interpreted as single, r-t points. In this case they need to have
the same shapes. Otherwise a structured r-t grid is created.
Default: ``True``
parts : :class:`int`, optional
Since the solution is calculated by setting the transmissivity to local
constant values, one needs to specify the number of partitions of the
transmissivity. Default: ``30``
lap_kwargs : :class:`dict` or :any:`None` optional
Dictionary for :any:`get_lap_inv` containing `method` and
`method_dict`. The default is equivalent to
``lap_kwargs = {"method": "stehfest", "method_dict": None}``.
Default: :any:`None`
Returns
-------
head : :class:`numpy.ndarray`
Array with all heads at the given radii and time-points.
Notes
-----
If you want to use cartesian coordiantes, just use the formula
``r = sqrt(x**2 + y**2)``
Examples
--------
>>> ext_theis_2d([10,100], [1,2,3], 0.001, 0.001, 1, 10, -0.001)
array([[-0.33737576, -0.17400123, -0.09489812],
[-0.58443489, -0.40847176, -0.31095166]])
"""
lap_kwargs = {} if lap_kwargs is None else lap_kwargs
# check the input
if r_well < 0.0:
raise ValueError("The wellradius needs to be >= 0")
if not r_bound > r_well:
raise ValueError("The upper boundary needs to be > well radius")
if not storage > 0.0:
raise ValueError("The Storage needs to be positive.")
if not trans_gmean > 0.0:
raise ValueError("The Transmissivity needs to be positive.")
if var < 0.0:
raise ValueError("The variance needs to be positive.")
if not len_scale > 0.0:
raise ValueError("The correlationlength needs to be positive.")
if T_well is not None and not T_well > 0.0:
raise ValueError("The well Transmissivity needs to be positive.")
if not prop > 0.0:
raise ValueError("The proportionality factor needs to be positive.")
if parts <= 1:
raise ValueError("The numbor of partitions needs to be at least 2")
if not 0.0 < far_err < 1.0:
raise ValueError(
"The relative error of Transmissivity needs to be within (0,1)")
# genearte rlast from a given relativ-error to farfield-transmissivity
r_last = T_CG_error(far_err, trans_gmean, var, len_scale, T_well, prop)
# generate the partition points
if r_last > r_well:
R_part = specialrange_cut(r_well, r_bound, parts + 1, r_last)
else:
R_part = np.array([r_well, r_bound])
# calculate the harmonic mean transmissivity values within each partition
T_part = annular_hmean(
T_CG,
R_part,
trans_gmean=trans_gmean,
var=var,
len_scale=len_scale,
T_well=T_well,
prop=prop,
)
T_well = T_CG(r_well, trans_gmean, var, len_scale, T_well, prop)
return ext_grf(
time=time,
rad=rad,
S_part=np.full_like(T_part, storage),
K_part=T_part,
R_part=R_part,
dim=2,
lat_ext=1,
rate=rate,
h_bound=h_bound,
K_well=T_well,
struc_grid=struc_grid,
lap_kwargs=lap_kwargs,
)
ax = fig.add_subplot(1, 1, 1)
cond_gmean = 1e-4 # the geometric mean of the transmissivity
len_scale = 5.0 # correlation length of the log-transmissivity
hurst = 0.5 # hurst coefficient
var = 1.0 # variance of the log-transmissivity
rate = -1e-4 # pumping rate
dim = 2.0 # using a fractional dimension
far_err = 0.01 # absolute error for cut-off
parts = 20 # number of partitions
# genearte rlast from a given relativ-error to farfield-conductivity
r_last_tpl = TPL_CG_error(far_err, cond_gmean, len_scale, hurst, var, dim=dim)
# generate the partition points (with cut-off)
R_part_tpl = specialrange_cut(0, np.inf, parts + 1, r_last_tpl)
# calculate the harmonic mean conductivity values within each partition
K_part_tpl = annular_hmean(
TPL_CG,
R_part_tpl,
ann_dim=dim,
cond_gmean=cond_gmean,
len_scale=len_scale,
hurst=hurst,
var=var,
dim=dim,
)
# genearte rlast from a given relativ-error to farfield-conductivity
r_last = T_CG_error(far_err, cond_gmean, var, len_scale)
# generate the partition points