Learning

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Software Tutorials

An Introduction to Python Scripting: Part 3

Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 3 focuses on modules and packages, with a focus on NumPy and Matplotlib.

Loops, Splitting, and Operators

When constructing or running simulations, you may want to query or modify values associated with all, or some of, the objects in your model (such as zones, nodes, blocks, balls, contacts, rockbolts, etc.). This may be to measure results like stress or displacement, to assign a calculated extra variable for plotting, or to adjust a property value. There are several ways to identify and navigate across all these objects using loops, splitting, and operators — with each one becoming easier and faster to execute. See how you can apply all of these approaches in a tutorial where a zone property is randomly assigned for strength variability throughout the model. You will also see how much easier and faster these approaches have become. Applying model property distributions via the PROPERTY command is also reviewed.

Creating Groups Interactively and Automatically using the Model Pane

In this tutorial, we review how to automatically skin models, identify and group zone faces, and interactively select and group zones and zone faces. This tutorial also illustrates using the Model Pane to interactively add a shell structural element along a tunnel.

Technical Papers

Deep Sublevel Cave Mining and Surface Influence

With increasing depth, higher stress and more difficult mining. With increasing depth is there more ground surface effects or less?

Using MINEDWto simulate pore pressure as input for FLAC3Dand 3DEC

It has become common practice to create a three-dimensional (3-D) geomechanical model for the analysis of rock stability.

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.