anaflow.flow.neuman2004

neuman2004(time, rad, storage, trans_gmean, var, len_scale, rate=-0.0001, r_well=0.0, r_bound=inf, h_bound=0.0, struc_grid=True, parts=30, fix_T_well=False, lap_kwargs=None)[source]

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. Presented in [Neuman2004].

Parameters:

  • time (numpy.ndarray) – Array with all time-points where the function should be evaluated.

  • rad (numpy.ndarray) – Array with all radii where the function should be evaluated.

  • storage (float) – Storage of the aquifer.

  • trans_gmean (float) – Geometric-mean transmissivity.

  • var (float) – Variance of log-transmissivity.

  • len_scale (float) – Correlation-length of log-transmissivity.

  • rate (float, optional) – Pumpingrate at the well. Default: -1e-4

  • r_well (float, optional) – Radius of the pumping-well. Default: 0.0

  • r_bound (float, optional) – Radius of the outer boundary of the aquifer. Default: np.inf

  • h_bound (float, optional) – Reference head at the outer boundary as well as initial condition. Default: 0.0

  • struc_grid (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 (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 (dict or None optional) – Dictionary for get_lap_inv containing method and method_dict. The default is equivalent to lap_kwargs = {"method": "stehfest", "method_dict": None}. Default: None

Returns:

head – Array with all heads at the given radii and time-points.

Return type:

numpy.ndarray

References

[Neuman2004]

Neuman, Shlomo P., Alberto Guadagnini, and Monica Riva. ‘’Type-curve estimation of statistical heterogeneity.’’ Water resources research 40.4, 2004