In this study,a common-node DEM-SPH coupling model based on the shared node method is proposed,and a fluid–structure coupling method using the common-node discrete element method-smoothed particle hydrodynamics(DS-SP...In this study,a common-node DEM-SPH coupling model based on the shared node method is proposed,and a fluid–structure coupling method using the common-node discrete element method-smoothed particle hydrodynamics(DS-SPH)method is developed using LS-DYNA software.The DEM and SPH are established on the same node to create common-node DEM-SPH particles,allowing for fluid–structure interactions.Numerical simulations of various scenarios,including water entry of a rigid sphere,dam-break propagation over wet beds,impact on an ice plate floating on water and ice accumulation on offshore structures,are conducted.The interaction between DS particles and SPH fluid and the crack generation mechanism and expansion characteristics of the ice plate under the interaction of structure and fluid are also studied.The results are compared with available data to verify the proposed coupling method.Notably,the simulation results demonstrated that controlling the cutoff pressure of internal SPH particles could effectively control particle splashing during ice crushing failure.展开更多
在复杂网络中高质量的社团划分会更好地揭示网络的结构特性与功能。基于节点相似性的算法是一类具有代表性的社团划分算法,但现有的基于节点相似性算法没有充分考虑到共邻节点之间的联系导致准确率下降。针对上述问题,首先定义共邻节点...在复杂网络中高质量的社团划分会更好地揭示网络的结构特性与功能。基于节点相似性的算法是一类具有代表性的社团划分算法,但现有的基于节点相似性算法没有充分考虑到共邻节点之间的联系导致准确率下降。针对上述问题,首先定义共邻节点贡献度概念,提出一种基于共邻节点贡献度的社团划分算法(Contribution of Common Neighbor Nodes Based Community Division Algorithm, CCNNA),将共邻节点之间的连边数参与到RA相似度指标的计算当中,提高了度量的准确性;然后运用改进的层次聚类与最优模块度值的思想实现网络的社团划分。在人工合成网络与真实网络上的实验结果表明,所提算法能够很好地挖掘社团结构,与模块度优化CNM(Clauset-Newman-Moore)算法以及最新的基于节点相似性算法相比,所提算法有更高的社团模块度和划分准确率。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52201323).
文摘In this study,a common-node DEM-SPH coupling model based on the shared node method is proposed,and a fluid–structure coupling method using the common-node discrete element method-smoothed particle hydrodynamics(DS-SPH)method is developed using LS-DYNA software.The DEM and SPH are established on the same node to create common-node DEM-SPH particles,allowing for fluid–structure interactions.Numerical simulations of various scenarios,including water entry of a rigid sphere,dam-break propagation over wet beds,impact on an ice plate floating on water and ice accumulation on offshore structures,are conducted.The interaction between DS particles and SPH fluid and the crack generation mechanism and expansion characteristics of the ice plate under the interaction of structure and fluid are also studied.The results are compared with available data to verify the proposed coupling method.Notably,the simulation results demonstrated that controlling the cutoff pressure of internal SPH particles could effectively control particle splashing during ice crushing failure.
文摘在复杂网络中高质量的社团划分会更好地揭示网络的结构特性与功能。基于节点相似性的算法是一类具有代表性的社团划分算法,但现有的基于节点相似性算法没有充分考虑到共邻节点之间的联系导致准确率下降。针对上述问题,首先定义共邻节点贡献度概念,提出一种基于共邻节点贡献度的社团划分算法(Contribution of Common Neighbor Nodes Based Community Division Algorithm, CCNNA),将共邻节点之间的连边数参与到RA相似度指标的计算当中,提高了度量的准确性;然后运用改进的层次聚类与最优模块度值的思想实现网络的社团划分。在人工合成网络与真实网络上的实验结果表明,所提算法能够很好地挖掘社团结构,与模块度优化CNM(Clauset-Newman-Moore)算法以及最新的基于节点相似性算法相比,所提算法有更高的社团模块度和划分准确率。
