Programme

Day 1
Discrete Element Methods for image‑based simulation

Most image-based workflows presented at IBSim convert 3D images into finite-element meshes. This training session introduces a complementary route: the Discrete Element Method (DEM), which represents matter as a set of interacting particles rather than a continuous mesh. That makes DEM especially well suited to the phenomena that are hardest to capture with FEM — crack initiation and propagation, fragmentation, granular flow, powder compaction, and diffuse damage inside a real, image-based microstructure. Every example will be built with a friencly python library or with GranOO, a free and open-source (GPLv3) C++/Python discrete element workbench developed by a French academic consortium (I2M / Arts et Métiers, Bordeaux — IRCER / University of Limoges — LAMIH / UPHF).

GranOO is not a black-box software but a modular collection of C++ libraries and Python bindings. Simulations are assembled as ordered plug-ins driven by simple XML input files, so a user can build a bespoke DEM model without rewriting a solver. It is an explicit dynamic DEM code embedding mechanical, thermal and electrical models (multiphysics), with cohesive-beam / lattice-spring formulations that let a discrete domain reproduce a continuous material — the key to bridging real microstructures and mechanical response.

Hands-on programme (4 modules)

1. DEM & GranOO — theory. Foundations of explicit DEM, the cohesive-beam model for continuous media, and how a GranOO simulation is assembled from plug-ins.
2. Powder compaction (friendly python libDEM). Granular packing and densification — directly relevant to additive manufacturing, powder-bed processes and sintering.
3. Image-based simulation at the microstructure scale (friendly python libDEM). The direct bridge to IBSim — turning a segmented µCT / tomographic volume into a DEM domain to simulate cracking, fragmentation and sintering in the actual microstructure.
4. Some Fracture simulations  (GranOO). DEM's flagship strength: multi-cracking and fragmentation (e.g. Brazilian test, impact on brittle media).

DEM complements image-based FEM precisely where meshes struggle: discontinuities. Recent peer-reviewed GranOO work already operates on this exact loop — image → microstructure → DEM — including ML-aided 3D microstructure reconstruction for viscous-flow sintering (Benjira et al., Powder Technology, 2026), 3D DEM of porous-cracked ceramics at the microstructure scale (Longchamp et al., J. Eur. Ceram. Soc., 2023), microstructure-scale dynamic fragmentation of porous-brittle materials (Longchamp et al., C. R. Mécanique, 2025), and powder spreading in additive manufacturing (Marchais et al., Comp. Part. Mech., 2021).

Day 2
A Full Mesh-Based DVC Pipeline: From Shape-Conforming Meshing to Robust 3D Strain Analysis 

Lead by Kamel Madi & Loic Courtois (3Dmagination, UK).  

This workshop provides a comprehensive introduction to in situ testing and FE-based Digital Volume Correlation (DVC). Participants will explore the critical parameters required to build a reliable DVC pipeline, spanning image pre-processing, mesh generation, and post-analysis. Attendees will gain hands-on experience running a complete, end-to-end workflow to extract 3D full-field displacement and strain maps. The session will demonstrate how to exploit this data to link a material’s internal morphology directly to its mechanical behaviour. We will also illustrate how mechanical regularization can be leveraged to successfully tackle difficult material textures and noisy image sets. Blending theoretical lectures with practical application, this session will guide attendees through real-world analysis using Thermo Scientific Avizo xDVC software.