In this work,a novel fluid-structure coupling method called the common node discrete element-smoothed particle hydrodynamics(DS-SPH)method is introduced.This framework combines the principles of the common node discre...In this work,a novel fluid-structure coupling method called the common node discrete element-smoothed particle hydrodynamics(DS-SPH)method is introduced.This framework combines the principles of the common node discrete element method(DEM)and smoothed particle hydrodynamics(SPH)to construct DEM-SPH particles situated on the same node.By doing so,the DEM particles can interact with the SPH particles within their support domain,enabling fluid-structure interaction(FSI).To determine the DEM microscopic parameters required for this method,uniaxial compression and three-point bending tests are conducted on sea ice.To verify the proposed model,we select the interaction between sea ice and structures as a case study.Through simulation,the model's capability of accurately depicting sea ice deformation and fracture has been demonstrated.The results indicate that the inclusion of SPH particles with fluid properties in the DEM model has minimal impact on the main mechanical parameters of sea ice.Additionally,it helps prevent the occurrence of particle splashing during cement failure.However,it is observed that the size of DEM particles and the friction between DEM particles and the structure significantly influence the macroscopic mechanical behavior of the common-node DEM-SPH model.Finally,we compare the fracture behavior of sea ice and the ice forces acting on structures obtained from the current model with on-site measured results.The agreement between the two sets of data is excellent,further validating the effectiveness of the proposed model in practical applications.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financially supported by the National Natural Science Foundation of China (Grant No.52201323)。
文摘In this work,a novel fluid-structure coupling method called the common node discrete element-smoothed particle hydrodynamics(DS-SPH)method is introduced.This framework combines the principles of the common node discrete element method(DEM)and smoothed particle hydrodynamics(SPH)to construct DEM-SPH particles situated on the same node.By doing so,the DEM particles can interact with the SPH particles within their support domain,enabling fluid-structure interaction(FSI).To determine the DEM microscopic parameters required for this method,uniaxial compression and three-point bending tests are conducted on sea ice.To verify the proposed model,we select the interaction between sea ice and structures as a case study.Through simulation,the model's capability of accurately depicting sea ice deformation and fracture has been demonstrated.The results indicate that the inclusion of SPH particles with fluid properties in the DEM model has minimal impact on the main mechanical parameters of sea ice.Additionally,it helps prevent the occurrence of particle splashing during cement failure.However,it is observed that the size of DEM particles and the friction between DEM particles and the structure significantly influence the macroscopic mechanical behavior of the common-node DEM-SPH model.Finally,we compare the fracture behavior of sea ice and the ice forces acting on structures obtained from the current model with on-site measured results.The agreement between the two sets of data is excellent,further validating the effectiveness of the proposed model in practical applications.
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。
