粒子(生态学)
内腐蚀
材料科学
土壤水分
岩土工程
计算
离散元法
粒径
机械
复合材料
地质学
数学
物理
土壤科学
古生物学
海洋学
算法
作者
Habib Taha,Ngoc‐Son Nguyen,Didier Marot,Abbas Hijazi,Khalil Abou-Saleh
标识
DOI:10.1016/j.gete.2021.100305
摘要
Modeling of suffusion in granular soils by using a fully coupled fluid-DEM model is still challenging due to its very high cost of computation. The particle removal approach is an alternative to mimic the erosion of fine particles by the seepage flow. In this paper, we study different particle removal methods and their impact on the mechanical behavior of eroded samples. Gap-graded samples with different fine contents f c were simulated by using the DEM. Fine particles are removed from the original sample under constant stress state by using three different methods: random removal, the method of Scholtès et al. based on the particle internal moment m p , and a new method proposed here that is based on the concept of weak and strong force networks. A micro-mechanical investigation into numerical samples shows that the fine particles have small contribution in carrying stresses when f c ≤ 30 % but they offer a great bracing effect to the coarse fraction, which allows the latter to carry high stresses. As a result, a removal of fine particles destroys greatly this bracing system, leading to a great reduction in the bearing capability of the coarse fraction. A comparison between the three methods shows that a removal of fine particles belonging to the weak force network has a lower impact on the mechanical behavior of eroded samples than the random removal. In addition, the method of Scholtès et al. gives results quite similar to those given by the random removal in terms of the shear strength of eroded samples. • The DEM study of the mechanical behavior of Gap-graded samples with different fine contents. • A new method for removing particles by considering the mechanical stresses applied on them. • The comparison of the impact on soil mechanical behavior of this method with two already published particle removal methods. • A micro-mechanical investigation in order to explain the different results.
科研通智能强力驱动
Strongly Powered by AbleSci AI