Dr Daniel Barreto Gonzalez, Edinburgh Napier University

The Discrete Element Method (DEM) is a numerical model that enables to replicate the behaviour of diverse particulate materials including sand, food grains, pharmaceutical products, and ceramic powders, amongst others. Current challenges for a more extended use of DEM include steep learning curves, as well as the inefficient representation of individual particle shapes, amongst others. Furthermore, millions of particles may be required to replicate a given problem. DEM has an enormous computational cost.

Dr Barreto will extend what can be achieved with DEM by tackling large-scale engineering problems validated experimentally whilst (i) accounting for complex particle-scale phenomena more realistically; and (ii) using machine learning and advanced statistical approaches to reduce calibration efforts, quantify uncertainties and produce better and quicker DEM analyses. This has the potential to influence professional/industrial practice by increasing reliability and enabling the use of lower factors of safety in design. Hence also reducing design and construction costs in engineering and increasing competitiveness across several disciplines.