NAFEMS Americas and Digital Engineering (DE) teamed up (once again) to present CAASE, the (now Virtual) Conference on Advancing Analysis & Simulation in Engineering, on June 16-18, 2020!

CAASE20 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, unlike any other, to share experiences, discuss relevant trends, discover common themes, and explore future issues, including:
-What is the future for engineering analysis and simulation?
-Where will it lead us in the next decade?
-How can designers and engineers realize its full potential?
What are the business, technological, and human enablers that will take past successful developments to new levels in the next ten years?

Resource Abstract

Very elongated (high aspect ratio) particles -- such as individual hay or hair strands -- are commonly found in chemical, agriculture, and manufacturing industry applications. In many of these cases, elongated particles may also be flexible, producing large deformation, bending and twisting due to the interaction with other particles or boundaries; or they may be under the action of a flow field. These particles are not always straight and may be composed of different branches or offshoots, for which each ramification has its own diameter and elastic properties.



This presentation provides an overview of fiber particle-shape model, a powerful tool for simulating the dynamics of a large number of fibers, both straight and multi-branched, depicting behaviors such as flexibility, deformation, and inter-fiber interaction. The presentation also introduces coupling between Rocky DEM and ANSYS Fluent, so you can learn how to accurately account for the effect of fluids on fibers through DEM-CFD coupled simulations. In addition, several application examples -- ranging from modeling hair strands flowing in a vacuum cleaner device to simulating hay compaction into storage bales -- prove that this approach is a useful tool for evaluating new designs and a variety of operating conditions, reducing the cost involved with new prototypes and experimental testing.