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Aprendizaje

Aprendizaje

Aprendizaje

Tutoriales de Software

Plotting Borehole Core Data using Geometry and FISH

In this example, you will see how to create your own custom plot of drill core data containing location, orientation, depth, and geotechnical data (lithography. fracture count, rock strength, weathering, and RMR).

Working with Building Blocks in FLAC3D 6 (Part 2)

This video demonstrates filling the empty space between key model elements and out to the far field boundary using Building Blocks in FLAC3D 6.

Using Python in Itasca Software

Python scripting is built into current versions of FLAC3D, 3DEC, and PFC. This video introduces users of Itasca software to working with Python and FLAC3D, 3DEC, and PFC types (zones, blocks, ball, structural elements, and so on). The Itasca Module, a comparison with FISH scripting, and object-oriented and array-oriented interfaces are reviewed and demonstrated.

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Artículos Técnicos

The Economic Challenges of Dewatering at the Victor Diamond Mine in Northern Ontario, Canada

The challenges of mining economically have never been greater than under current global financial conditions.

Which fractures are imaged with Ground Penetrating Radar? Results from an experiment in the Äspö Hardrock Laboratory, Sweden

Identifying fractures in the subsurface is crucial for many geomechanical and hydrogeological applications. Here, we assess the ability of the Ground Penetrating Radar (GPR) method to image open fractures with sub-mm apertures in the context of future deep disposal of radioactive waste.

Connectivity, permeability, and channeling in randomly distributed and kinematically defined discrete fracture network models

A major use of DFN models for industrial applications is to evaluate permeability and flow structure in hardrock aquifers from geological observations of fracture networks. The relationship between the statistical fracture density distributions and permeability has been extensively studied, but there has been little interest in the spatial structure of DFN models, which is generally assumed to be spatially random (i.e., Poisson). In this paper, we compare the predictions of Poisson DFNs to new DFN models where fractures result from a growth process defined by simplified kinematic rules for nucleation, growth, and fracture arrest.

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