Export citation

Export citation

Choose format for download:

Download Citation

    Edwards thermodynamic framework controls density segregation in cyclically sheared granular materials

    Haiyang Lu1, Houfei Yuan1, Shuyang Zhang1, Zhikun Zeng1, Yi Xing1, Jiazhao Xu1, Xin Wang1, and Yujie Wang1,2,3,*

    • *Contact author: yujiewang@sjtu.edu.cn

    Phys. Rev. E 113, 065407 – Published 9 June, 2026

    DOI: https://doi.org/10.1103/zd3d-7ztp

    Abstract

    Granular segregation is a widespread phenomenon observed in various industrial applications, geophysical processes, and daily life events. It can be driven by multiple mechanisms, and numerous models have been proposed to elucidate this behavior. However, most of these models are phenomenological and qualitative, lacking a unified theoretical framework based on statistical mechanics. Using x-ray tomography, we experimentally investigate granular segregation phenomena in a mixture of particles with different densities under quasistatic cyclic shear. Our findings demonstrate that in regions where particles have sufficient accumulated strain for structural relaxation, their steady-state height distributions can be quantitatively characterized by minimizing an effective free energy based on a segregation temperature that captures the competition between the mixing entropy and gravitational potential energy. We find this temperature coincides with Edwards’ compactivity within error under various pressures and cyclic shear amplitudes. Therefore, we find that granular segregation in quasistatic conditions can be fundamentally explained by an effective granular thermodynamic framework including real energy terms based on the Edwards statistical ensemble.

    Physics Subject Headings (PhySH)

    Authorization Required

    We need you to provide your credentials before accessing this content.

    Supplemental Material (Subscription Required)

    References (Subscription Required)

    Outline

    Information

    Sign In to Your Journals Account

    Filter

    Filter

    Article Lookup

    Enter a citation