Learning

Software Tutorials

FLAC3D 6.0 PFC Plugin Conveyor

Using Python in FLAC3D 6

The Python programming language is embedded inside FLAC3D 6 and extended to allow FLAC3D models to be manipulated from Python programs. This webinar recording provides a brief introduction to Python scripting and includes many examples of using Python with FLAC3D.

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

Use of a Chemical Transport Code for the Prediction of Gold Heap Leach Production

Itasca Denver, Inc., (Itasca) in conjunction with Newmont Mining Corporation (NMC) developed a numerical model to estimate gold (Au) production from NMC’s heap-leach operations.

Time-Dependent Behavior of Saint-Martin-La-Porte Exploratory Galleries: Field Data Processing and Numerical Modeling of Excavation in Squeezing Rock Conditions

Field monitoring programs (e.g., convergence measurements and stress measurements in the support system) play an important role in following the response of the ground and of the support system during and after excavation. They contribute to the adaptation of the excavation and support installation method and the prediction of the long-term behavior. In the context of the Lyon–Turin link project, an access gallery (SMP2) was excavated between 2003 and 2010, and a survey gallery (SMP4) has been excavated since 2017.

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.