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.

翻译：计算能力的进步使得对大规模流体力学和/或颗粒系统进行数值仿真成为可能，其中许多系统是核心工业流程不可或缺的组成部分。在现有的不同数值方法中，离散元法（DEM）为涉及颗粒与非连续材料的各类物理系统提供了最精确的表述之一。因此，DEM已成为解决与颗粒流及粉末力学相关工程问题的一种广受认可的方法。此外，DEM可与基于网格的计算流体力学（CFD）方法结合，从而实现对诸如流化床中发生的化学过程的仿真。然而，由于颗粒系统固有的多尺度特性，DEM计算量巨大，限制了仿真时长或颗粒数量。为此，NeuralDEM提出了一种端到端方法，用快速、适应性强的深度学习代理模型替代缓慢的数值DEM计算流程。NeuralDEM能够利用宏观观测量描绘不同状态下的长期输运过程，而无需参考任何微观模型参数。首先，NeuralDEM将DEM的拉格朗日离散化视为底层连续场，同时直接将宏观行为建模为附加辅助场。其次，NeuralDEM引入了可扩展至工业规模场景实时建模的多分支神经算子——涵盖从缓慢伪稳态到快速瞬态的各种工况。此类场景此前对深度学习模型构成了难以逾越的挑战。值得注意的是，NeuralDEM精确建模了包含16万CFD网格与50万DEM颗粒的耦合CFD-DEM流化床反应器，仿真轨迹时长达28秒。NeuralDEM将为先进工程与更快的工艺周期开启诸多新途径。