Learning

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Software Tutorials

Working with Email in Itasca Software

Learn how you can use commands and functions to send email messages and attachments via Itasca software. Use this capability to inform you when a model has finished running, a result is available (even attach a plot), or the model run is interrupted.

MINEDW Tutorial (Part 3: Boundary Conditions)

In this tutorial we will take a look at the different boundary conditions available to the user, and we will go over some examples of different scenarios in which they would be used.

An Introduction to Python Scripting: Part 1

Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 1 focuses on installing Python, variables and types, conditions and loops, and functions.

Technical Papers

Input to Orepass Design — A Numerical Modeling Study

Orepass design guidelines required for potentially continued mining at depth. Rock strength and stress state were validated through comparison with observed fallouts in orepasses and shafts and the optimal orientation and location of orepasses for future mining were determined.

A Discrete Fracture Network Model With Stress-Driven Nucleation: Impact on Clustering, Connectivity, and Topology

The realism of Discrete Fracture Network (DFN) models relies on the spatial organization of fractures, which is not issued by purely stochastic DFN models. In this study, we introduce correlations between fractures by enhancing the genetic model (UFM) of Davy et al. [1] based on simplified concepts of nucleation, growth and arrest with hierarchical rules.

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.