Björn Kaiser

FEFLOW can handle 3D *.dxf files. In 3D views, 3D *.dxf maps can be shown in 3D.

Drawings in AutoCAD have to use the same coordinate system as the FEFLOW model to ensure that the *.dxf map is shown in the right location in FEFLOW.

The *.dxf format supports a vast amount of different drawing components, including components that are only readable with specific 3rd party software. FEFLOW can deal with the most widely used and openly documented formats.

The assignment of time-varying groundwater recharge as an in/outflow on top/bottom material property follows the same concept like any other material property assignment. Depending on the format/structure/architecture of available data FEFLOW provides a broad variety of possibilities to input these data. A detailed description is provided by the FEFLOW Help => Contents => How to => Model Setup => Assignment of Time-Varying Material Properties.

If you want to check whether or not time-varying recharge is assigned properly FEFLOW provides the option to browse through the periods in the legend. Please note, that FEFLOW does not update time-varying material properties graphically while you are simulating. However, there is a workaround if you want to co-process the data during the simulation. You could add a Nodal Expression and refer to the time-varying material property of interest (e.g. in/outflow on top/bottom).

I suggest to represent the river with a 3rd kind Boundary Condition (Cauchy-BC). A Cauchy-BC applies a pre-defined reference head combined with a conductance parameter (transfer-rate).

Then, you could impose several time-series of the water levels as reference heads. Usually, measured time-series are available from local gauges. To regionalize water levels along the river you could use a 1D interpolation. The 1D linear interpolation is a method for interpolating linearly along line features provided by a map.

Floodwaves are damped by natural or artificial retention. To mimic the impact of floodwaves and retention on the groundwater you could take a time-dependent interpolation into account in addition to the spatial 1D-interpolation along the river.

However, this approximation is pure mathematical interpolation. If you are interested to represent the “real” physics of retention (including hysteresis) I suggest to couple FEFLOW with another numerical simulator, which is capable to solve the shallow water equations (St-Venant equation). In this context, an interface between FEFLOW and MIKE11 exists.

[b]How can I see the hydraulic head in the Karst only ?
The result of the observation point gives the HH in the karst or in the element?[/b]
If you locate observation points on mesh nodes which are additionally affected by Discrete Features you take both the DF and the porous media into account. A discrimination between Discrete Features and the porous medium does not exist for observation wells. However, the calculated heads at nodes occupied with Discrete Features are strongly influenced by the highly conductive Discrete Feature. Accordingly, if you record the heads on nodes which are additionally affected by Discrete Features you actually take the Karst into account.

[b]Is it a better method to create an additional layer with the a high enough K rather than using the DF ?[/b]
It depends on the model conceptualization and the question being investigated. Both methods have pros and cons. Without doubt, a fully discretized approach allows the representation of more details within the boundaries (limitations) of the applied flow law validity. However, a fully discretized approach inevitably results in a higher computational effort due an increased number of elements.

Discrete Features provide a useful alternative to represent different spatial scales for which an equivalent single Representative Elementary Volume does not exist anymore. In this case, two different continua, one for the porous medium and one for the Discrete Feature can be applied. At the same time, this method allows you to keep the number of elements at a lower level with respect to the fully discretized approach.

A manual addressing plug-in development is provided by the <a href="http://www.mikepoweredbydhi.com/-/media/shared%20content/mike%20by%20dhi/flyers%20and%20pdf/product-documentation/feflow%206.2%20user%20manual.pdf">FEFLOW Users Manual</a>, Chapter 16 entitled [b]Plug-ins and Interface Manager IFM[/b]:

Available callbacks and API functions are listed in the FEFLOW Help.

Could you please update to the latest FEFLOW 6.2 [i]Patch 9[/i] and try to reproduce the error message? In the main menu, please click on [b]Help[/b] and then on [b]About[/b]. Finally, click on the button [b]Check for Updates[/b].

Parameters which can have negative values can be estimated by PEST. The only problem is, is that you cannot log transform them.

However there is a way to still use it. Set the initial value to a positive value. Then please make the lower and upper bounds positive, but set the SCALE option to -1. In this way, the parameter is set to the negative of its value, just before it is set to the FEFLOW model.

As log transforming a parameter is recommended you probably should do this.

I suggest a heat-transfer BC (Cauchy). A heat transfer boundary condition applies a pre-defined reference temperature (e.g. surface temperature) combined with a transfer-rate.

The Parameter [b]Time[/b] represent the simulation time. In the fem-file [b]Time[/b] is always 0. Accordingly, if you assign an expression containing [b]Time[/b] you assign 0. This is attributed to the technical fact that you cannot assign time-dependent values to boundary conditions. Instead, you can either assign a constant value or you can assign a time-series-ID pointing to an existing time-series in the Time Series Editor. Due to this technical consideration, I suggest to prepare a time-series with a unique ID. Then assign this time-series-(ID) to the boundary condition.

Which set of numerical settings are generally likely to produce the most accurate results is ambitious to assess. Each model is unique with respect to the configuration/combination of the question being investigated, the problem class, the model conceptualization, the spatiotemporal discretization and so on. Therefore I try to give a rather general answer.

The difference between the [b]divergent form[/b] and the [b]convective form[/b] of the transport equation is mainly given by the convective term. The convective term of the divergent transport equation involves a divergent expression of the velocity field and the species/heat. In contrast, the convective term of the convective transport equation takes a gradient relationship into account. Both forms are physically equivalent. However, in their discretized forms these two equations lead to different formulations of Boundary Conditions (BC), which may or may not result into distinct budgets. In a practical sense, a Mass-flux BC (Neumann) and a Mass nodal sink/source BC take advective and dispersive transport into account if you solve the system by the divergent transport equation. In contrast, if you adopt the same BC’s, but solve the system by the convective form you only take the dispersive transport component into account. Under certain circumstances the divergent form may also lead to instabilities at "free-outflow" boundaries.

In some situations where the advective driver overwhelms the diffusive/dispersive driver the objective of a bounded numerical solution cannot always be satisfied. [b]Upwinding[/b] techniques may help to get a bounded solution by stabilizing the oscillating numerical behavior by non-physical dispersion. However, Upwinding techniques have the disadvantage with respect to the accuracy, because stability does not imply accuracy. The different Upwinding options differ in the context of how numerical dispersion is introduced. For example, the Streamline Upwind method stabilizes the solution where advection is dominating. The stabilization spatially correlates with the advective trajectory, while stabilization in transversal direction is neglected, thus crosswind damping is neglected. In contrast, the Shock Capturing method takes the gradients of the species/heat into account rather than the trajectory of groundwater flow. Accordingly, Shock Capturing works in both the longitudinal and transversal direction respectively. In a practical sense, I would try to solve the problem without Upwinding. Instead, I would try to improve the model (e.g. mesh). If all model improvements do result in the solution I expect, I would possibly intend to use the “most harmless” Upwinding.

Regarding the options for the predictor-corrector scheme FEFLOW provides the possibility to impose additional time-step constraints. Additional time-step constraints are provided by a [b]growth factor between subsequent time-steps[/b] and a [b]maximum time-step size[/b]. This option is required if the automatic predictor-corrector returns too large time-steps with respect to the required temporal accuracy. This situation is rarely the case. However, in some applications additional time-step constraints may become useful. A possible application is density-dependent flow. Another application are geothermal simulations involving Borehole Heat Exchangers (BHE’s) where the inlet temperature at a specific time-step is calculated from the outlet temperature of the previous time-step (e.g. temperature difference or power).

I hope this rather general answer helps.