This conference paper was submitted for presentation at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

Computational modelling is invaluable in solving complex problems in various industries. But effective modelling requires balancing multiple, often conflicting, attributes such as accuracy, speed, simplicity, and predictive power. These requirements invariably come with trade-offs: a more accurate model might be slower or more complex; a faster model may have limited predictive capability, and so on. In this context, what makes a model good? Successful model development needs to account for three axes: the needs of the product, the people, and its impact and purpose. On the product side, open-source innovation is a cornerstone of modern computational modelling. By fostering collaboration and allowing users to leverage existing tools, improving and modifying upon them, these open-source ecosystems can lead to massive improvements in modelling capabilities. Rather than revolutionary ideas driving innovation, it is these incremental enhancements leading to great progress. This work demonstrates a plethora of examples of the application of Discrete Element Method (DEM) to solve problems in a wide range of industries, from pharmaceuticals to materials processing, generating invaluable insights for process optimization and tackling complex problems. Scientists and engineers can be empowered by modelling frameworks, centralized modelling platforms where expertise can be shared and workflows streamlined, allowing people to focus on solving application specific problems. This work provides a review of varied modelling approaches taken by the authors over the years, encompassing many different industries, such as mixing applications, battery manufacturing, extruders, pharmaceutical drug manufacturing, agricultural machinery, and so on. It also outlines the concept of an in-development cloud-based calculator platform for facilitating collaboration and innovation in science and engineering. The importance of modelling lies in its broader purpose: the generation of solutions that balance efficiency, precision, and practicality. By emphasizing incremental improvement, open collaboration, and adaptability, computational modelling provides a robust foundation for addressing the multifaceted challenges faced by modern industries.