Challenges and AI-powered strategies for multi-scale superquadric DEM-CFD modeling of non-spherical particles

Abstract

Multi-scale superquadric discrete element method-computational fluid dynamics (DEM-CFD) coupling provides a powerful tool to resolve cross-scale behaviors inside fluidized bed reactors, yet several critical obstacles restrict its industrial application, including anisotropic contact hydrodynamics, incomplete heat transfer frameworks, immature non-spherical reaction models, and high computational cost for large-scale particle systems. This view systematically dissects the above bottlenecks, summarizes unresolved research gaps, and proposes targeted remedies: reduced-space Newton iteration for contact singularity, generalized shape-factor Nusselt correlations, superquadric coordinate-based shrinking core reaction models, and the synergy of coarse-grained particle models with graphics processing unit acceleration. Furthermore, this work elaborates on the transformative potential of artificial intelligence: deep learning can construct universal drag/heat transfer correlations from direct numerical simulation data, generate interpretable chemical reaction kinetics, and accelerate stiff solvers to cut simulation overhead. This view delivers clear development roadmaps for the next-generation multi-scale SuperDEM-CFD platform, supporting predictive design and real-time digital twin construction of gas-solid reactors.

Graphical abstract

Keywords

multi-scale simulation / non-spherical / superquadric DEM-CFD / gas–solid multiphase flow / AI-powered modeling

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