Python in Itasca Software
Online11-06-2025 - 12-06-2025
This course provides an overview of the Python programming language in Itasca software.
The course covers major applications of Python to extend modeling capabilities with the Itasca codes through many applied examples.
IMAT Training: Revolutionizing Mining Analysis with Seismology & Numerical Modeling
Minneapolis, Minnesota, United States16-06-2025 - 18-06-2025
Explore IMAT’s latest upgrade, uniting open-pit and underground mining capabilities for faster, smarter, and more efficient modeling.
Analyses of Embankment Dams and Slopes using FLAC2D/3D
Online28-05-2025 - 20-06-2025
This course will lead participants in using FLAC2D and FLAC3D to conduct complex analyses of embankment dams and slopes. The training is composed of multiple presentations and step-by-step tutorials.
Tutoriales de Software
FLAC3D 7.0 Structured Mesh Tutorial
A tutorial showing how to create a structured mesh in FLAC3D 7.0 using the extruder pane.
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.
Converting Plots to Data Files
Any model plot that you create interactively by adding plot-items and adjusting settings can be represented by an equivalent set of commands. This is useful should you want to include command-driven plotting in your modeling run.
Artículos Técnicos
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?
Tunneldrivning i heterogena förhållanden
InledningProblem: Brist på erfarenhet av tunneldrivning i heterogena förhållanden med konventionell uttagsteknik (borrning och sprängning).
Mål: Fördjupa kunskapen och förståelse av brott och deformationsmönster vid dessa förhållanden.
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.