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Key outcomes | D1.1. (M9)–Short list of academic reference problems (including their full definition in terms of initial configuration, boundary condition, time step, expected results, and/or analytical solutions, etc.). The first list relates to problems that may be tackled by considering various hardware architectures, the second one to model-based challenges. Lists will be compiled as part of a short technical report and made available via the wiki and repositories. D1.2. (M12, M24, M36 and M48) – Implementation of hardware- and model-based short-list problems using at least two open-source DEM codes to give a comprehensive comparison of the different investigated solutions. Two individual problems will be considered each year (i.e. one hardware-based, another one model-based) and summarised in individual technical reports (M12, M24, M36 and M48). These will also be made available as individual datasets fully documented and shared via repositories. D1.3. (M48) – Two journal papers considering the conclusions and findings related to (i) hardware-based solutions and (ii) model-based solutions, respectively. | ||||||
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\uD83E\uDD14 Problem Statement
This working group will focus on solutions to gain orders of magnitude for tackling simulations at scales which are relevant for real life applications. Below are three approaches to achieve this:
Using high-performance computing and taking benefits of new hardware architectures.
Well-known solutions such as parallelisation with both CPU and GPU will be investigated. The efficient use of these solutions in particular contexts is still challenging. For instance, simulating non-spherical particles with GPU-based algorithm, handling load balancing on CPU parallel architecture for simulations or parallelising contact algorithms such as Voronoi based searching remains difficult and are still scientific barriers. The potential participation of hardware manufacturers will enable new insights for the optimisation of DEM algorithms.
Using model-based solutions
These could be
enriched contact models;
space periodicity; and/or time extrapolation strategies.
The first approach consists in enriching the contact model in order to do more with fewer particles. The second and third approaches take advantage of temporal and spatial periodicities to use less particles or time steps.
Of course, other promising solutions which are not listed here may be investigated such as machine-learning based techniques.
🎯 Scope
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\uD83D\uDDD3 Timeline
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\uD83D\uDEA9 Milestones and deadlines
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\uD83D\uDD17 Reference materials
Important Pages
👋 Meet the team
Nicolin Govender
WG1 & DEM-HPC sub-group lead
Hongyang Cheng
DEM-UP sub-group lead
Deepak Tunuguntla
DEM-ML sub-group lead