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.
Python scripting is built into current versions of FLAC3D, 3DEC, and PFC. This video introduces users of Itasca software to working with Python and FLAC3D, 3DEC, and PFC types (zones, blocks, ball, structural elements, and so on). The Itasca Module, a comparison with FISH scripting, and object-oriented and array-oriented interfaces are reviewed and demonstrated.
This tutorial will show how to paint zone data onto an imported geometric surface in FLAC3D.
Lahars represent natural phenomena that can generate severe damage in densely populated urban areas. The evaluation of pressures generated by these mass flows on constructions (buildings, infrastructure…) is crucial for civil protection and assessment of physical vulnerability. The existing tools to model the spread of flows at large scale in densely populated urban areas remain inaccurate in the estimation of mechanical efforts. A discrete numerical model is developed for evaluating debris flow (DF) impact pressures at the local scale of one structure.
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.
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.