Mechanical behaviors of granular materials are complicated and greatly influenced by the particle shape.Current,some composite approaches have been proposed for realistic particle shape modelling within discrete eleme...Mechanical behaviors of granular materials are complicated and greatly influenced by the particle shape.Current,some composite approaches have been proposed for realistic particle shape modelling within discrete element method(DEM),while they cannot give a good representation to the shape and mass properties of a real particle.In this work,a novel algorithm is developed to model an arbitrary particle using a cluster of non-overlapping disks.The algorithm mainly consists of two components:boundary filling and domain filling.In the boundary filling,some disks are placed along the boundary for a precise representation of the particle shape,and some more disks are placed in the domain to give an approximation to the mass properties of the particle in the domain filling.Besides,a simple method is proposed to correct the mass properties of a cluster after domain filling and reduce the number of the disks in a cluster for lower computational load.Moreover,it is another great merit of the algorithm that a cluster generated by the algorithm can be used to simulate the particle breakage because of no overlaps between the disks in a cluster.Finally,several examples are used to show the robust performance of the algorithm.A current FORTRAN version of the algorithm is available by contacting the author.展开更多
In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Par...In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Pareto distribution （GPD） in extreme value theory was used to fit the extreme pollution concentrations of three main pollutants： PM10, NO2 and SO：, from 2005 to 2010 in Changsha, China. Secondly, the prediction results were compared with actual data by a scatter plot. Four statistical indicators： EMA （mean absolute error）, ERMS （root mean square error）, IA （index of agreement） and R2 （coefficient of determination） were used to evaluate the goodness-of-fit as well. Thirdly, the return levels corresponding to different return periods were calculated by the fitted distributions. The fitting results show that the distribution of PM10 and SO2 belongs to exponential distribution with a short tail while that of the NOe belongs to beta distribution with a bounded tail. The scatter plot and four statistical indicators suggest that GPD agrees well with the actual data. Therefore, the fitted distribution is reliable to predict the return levels corresponding to different return periods. The predicted return levels suggest that the intensity of coming pollution events for PM10 and SO2 will be even worse in the future, which means people have to get enough preparation for them.展开更多
基金Project(2011CB013504)supported by the National Basic Research Program(973 Program)of ChinaProject(2013BAB06B01)supported by Key Projects in the National Science&Technology Pillar Program during the Twelfth Five-year Plan Period,China+1 种基金Projects(51309089,51479049)supported by National Natural Science Foundation of ChinaProject(487237)supported by Natural Sciences and Engineering Research Council of Canada
文摘Mechanical behaviors of granular materials are complicated and greatly influenced by the particle shape.Current,some composite approaches have been proposed for realistic particle shape modelling within discrete element method(DEM),while they cannot give a good representation to the shape and mass properties of a real particle.In this work,a novel algorithm is developed to model an arbitrary particle using a cluster of non-overlapping disks.The algorithm mainly consists of two components:boundary filling and domain filling.In the boundary filling,some disks are placed along the boundary for a precise representation of the particle shape,and some more disks are placed in the domain to give an approximation to the mass properties of the particle in the domain filling.Besides,a simple method is proposed to correct the mass properties of a cluster after domain filling and reduce the number of the disks in a cluster for lower computational load.Moreover,it is another great merit of the algorithm that a cluster generated by the algorithm can be used to simulate the particle breakage because of no overlaps between the disks in a cluster.Finally,several examples are used to show the robust performance of the algorithm.A current FORTRAN version of the algorithm is available by contacting the author.
基金Project(51178466) supported by the National Natural Science Foundation of ChinaProject(200545) supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China+1 种基金Project(2011JQ006) supported by the Fundamental Research Funds of the Central Universities of ChinaProject(2008BAJ12B03) supported by the National Key Program of Scientific and Technical Supporting Programs of China
文摘In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Pareto distribution （GPD） in extreme value theory was used to fit the extreme pollution concentrations of three main pollutants： PM10, NO2 and SO：, from 2005 to 2010 in Changsha, China. Secondly, the prediction results were compared with actual data by a scatter plot. Four statistical indicators： EMA （mean absolute error）, ERMS （root mean square error）, IA （index of agreement） and R2 （coefficient of determination） were used to evaluate the goodness-of-fit as well. Thirdly, the return levels corresponding to different return periods were calculated by the fitted distributions. The fitting results show that the distribution of PM10 and SO2 belongs to exponential distribution with a short tail while that of the NOe belongs to beta distribution with a bounded tail. The scatter plot and four statistical indicators suggest that GPD agrees well with the actual data. Therefore, the fitted distribution is reliable to predict the return levels corresponding to different return periods. The predicted return levels suggest that the intensity of coming pollution events for PM10 and SO2 will be even worse in the future, which means people have to get enough preparation for them.
