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Software Tutorials

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.

MINEDW Tutorial (Part 4: Meshing)

In this tutorial we will go over meshing, from the creation of a 2D mesh and how to import it to MINEDW, to the inclusion of topography, layers, and pinch-outs to different areas of interest in the model.

Bonded Block Model undergoing Damage and Bulking during Simulated Relaxation

Continuum numerical modeling is inherently limited when the rock behavior involves mechanisms such as spalling and bulking. The Bonded Block Model (BBM) approach simulates the initiation of cracks that can coalesce and/or propagate leading to extension and shear fracturing, as well as the rock (e.g., intact, jointed, or veined) strength dependency on confinement.

Technical Papers

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.

Blast Movement Simulation Through a Hybrid Approach of Continuum, Discontinuum, and Machine Learning Modeling

This work presents a hybrid modeling approach to efficiently estimate and optimize rock movement during blasting. A small-scale continuum model simulates early-stage, near-field blasting physics and generates synthetic data to train a machine learning (ML) model. Key parameters such as expanded hole diameter, burden velocity, and gas pressure are obtained through the ML model, which then inform a discontinuum model to predict far-field muckpile formation. The approach captures essential blast physics while significantly accelerating blast design optimization.

A Calibrated Predictive Geochemical Model of Leaching and Attenuation Reactions in a Mine Pit Lake

A geochemical model was developed to predict future water quality of the Cove pit lake in support of site closure and regulatory permitting.