Hydrogen, Fuel Cell & Energy Storage

Hydrogen, Fuel Cell & Energy Storage

Discrete Element Method (DEM) Analysis of Thermal Expansion-Induced Stress in Granular Media

Document Type : Research Paper

Authors
1 Northern Research Center for Science and Technology, Malek Ashtar University of Technology
2 Babol Noshirvani University of Technology, Mechanical Engineering Faculty, Babol, Iran
10.22104/hfe.2026.8177.1396
Abstract
Minor temperature variations are often considered negligible in the mechanical design and operation of confined granular systems. However, due to the discrete nature of particle interactions, even small thermally induced expansions can generate significant contact stresses within granular assemblies. This study focuses on thermal-driven stress in mono-disperse isotropic granular materials using discrete element method based on the Hertz-Mindlin no-slip contact model. Thermal loading is introduced through uniform radial expansion of particles, allowing direct assessment of contact force and pressure evolution under controlled temperature changes. The model is validated against COMSOL Multiphysics, ensuring accurate quantification of contact-level stresses. Furthermore, a parametric study is conducted to evaluate influence of particle size, enclosure slenderness ratio, and solid fraction on the resulting contact stresses. The results show that even minor temperature increment can induce contact pressures of considerable magnitude, which may locally approach or exceed yield strength of the particle material. These findings indicate that such thermal stresses are not merely secondary effects but critical factors in the mechanical performance of granular assemblies. In addition, while particle size exhibits a negligible effect on contact pressure due to compensating changes in contact force and contact area, both the slenderness ratio and solid fraction show an approximately linear correlation with the induced stresses, indicating a strong dependance on geometric confinement and packing density. To extend the predictive capability of the numerical model and reduce computational cost, an artificial neural network is trained using the DEM results, demonstrating high accuracy in predicting particle-particle and particle-wall contact pressures.
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Articles in Press, Accepted Manuscript
Available Online from 16 September 2026