Generate a Hybrid Mesh by Combining Block Ranger and GVol

This tutorial will demonstrate a method to create a hybrid mesh of tetrahedral zones to model the rock mass and hexahedral zones to model a concrete liner. Hexahedral zones for the liner are preferred in order to more accurately capture plastic strains in this region. The meshing is done by utilizing the Itasca Griddle volume mesher plug-in for Rhino 3D. Importing the final mesh into FLAC3D, for future finite volume modeling, is also demonstrated.

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.

MINEDW Tutorial (Part 1: Menu Options)

In this tutorial we will briefly cover the MINEDW user interface, its components, and the MINEDW Menu with the different options and tools it provides to build numerical models.

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.

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.

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.