Authors: Benedikt Alkin, Tobias Kronlachner, Samuele Papa, Stefan Pirker, Thomas Lichtenegger, Johannes Brandstetter
Abstract: Advancements in computing power have made it possible to numerically simulate
large-scale fluid-mechanical and/or particulate systems, many of which are
integral to core industrial processes. Among the different numerical methods
available, the discrete element method (DEM) provides one of the most accurate
representations of a wide range of physical systems involving granular and
discontinuous materials. Consequently, DEM has become a widely accepted
approach for tackling engineering problems connected to granular flows and
powder mechanics. Additionally, DEM can be integrated with grid-based
computational fluid dynamics (CFD) methods, enabling the simulation of chemical
processes taking place, e.g., in fluidized beds. However, DEM is
computationally intensive because of the intrinsic multiscale nature of
particulate systems, restricting simulation duration or number of particles.
Towards this end, NeuralDEM presents an end-to-end approach to replace slow
numerical DEM routines with fast, adaptable deep learning surrogates. NeuralDEM
is capable of picturing long-term transport processes across different regimes
using macroscopic observables without any reference to microscopic model
parameters. First, NeuralDEM treats the Lagrangian discretization of DEM as an
underlying continuous field, while simultaneously modeling macroscopic behavior
directly as additional auxiliary fields. Second, NeuralDEM introduces
multi-branch neural operators scalable to real-time modeling of
industrially-sized scenarios – from slow and pseudo-steady to fast and
transient. Such scenarios have previously posed insurmountable challenges for
deep learning models. Notably, NeuralDEM faithfully models coupled CFD-DEM
fluidized bed reactors of 160k CFD cells and 500k DEM particles for
trajectories of 28s. NeuralDEM will open many new doors to advanced engineering
and much faster process cycles.
Source: http://arxiv.org/abs/2411.09678v1
