DEM study and machine learning model of particle percolation under vibration

This paper aims to understand model the effect of vibration on particle percolation. The percolation of small particles in a vibrated bed of big particles is studied by DEM. It is found the percolation velocity (V_p) decreases with increasing vibration amplitude (A) and frequency (f) when the size ratio of small to large particles (d/D) is smaller than the spontaneous percolation threshold of 0.154. Vibration can enable percolation when the size ratio is larger than 0.154, while V_p increases with increasing A and f first and then decreases. V_p can be correlated to the vibration velocity amplitude under a given size ratio. Previous radial dispersion model can still be applied while the dispersion coefficient is affected by vibration conditions and size ratio. Furthermore, a machine learning model is trained to predict V_p as a function of A, f and d/D, and is then used to obtain the percolation threshold size ratio as a function of vibration conditions.     

Numerical study of forced axial segregation of binary density granular system in a split rotary drum

Driving and controlling the segregation in a rotary drum is both a theoretical and a practical challenge in powder technology. A novel horizontal split drum design composed of two reverse rotating sub-drums was explored to drive axial segregation of granular matter. DEM (Discrete Element Method) simulations were performed to study the particle dynamics and segregation performance of binary density particles in the split drum. Then, the effects of drum speed, the speed ratio of the two sub-drums, and the split position on the axial segregation were analyzed. It was found that true axial segregation occurred in the split drum and heavier particles tend to accumulate in the region near the split. An increase in drum speed can accelerate the segregation but it has no obvious influence on the final axial distribution of particles. The results obtained indicate when two sub-drums rotate at different speeds, the concentrated region of heavier particles moves towards the low-speed sub-drum. These findings could lead to new designs for a broad range of particle processing industries.     

Validation of microparameters in discrete element modeling of sea ice failure process

A GPU-based discrete element method (DEM) with bonded particles is investigated to simulate the mechanical properties of sea ice in uniaxial compressive and three-point bending tests. Both the uniaxial compressive strength and flexural strength of sea ice are related to the microparameters in DEM simulation including particle size, sample size, bonding strength, and interparticle friction coefficient. These parameters are analyzed to build the relationship between the material macrostrengths of sea ice and the microparameters of the numerical model in DEM simulations. Based on this relationship, the reasonable microparameters can be calculated by given macrostrengths in the applications of simulating the failure processes of sea ice. In this simulation, both uniaxial compressive strength and flexural strength of ice increase with the increasing ratio of sample size and particle size. The interparticle friction coefficient is directly related to the compressive strength but has little effect on the flexural strength. In addition, numerical simulations are compared with experimental data to show the performance of the proposed model, and a satisfactory agreement is achieved. Therefore, this microparameter validation approach based on macrostrengths can be applied to simulate the complicated failure process of sea ice interacting with offshore platform structures.     

DDRM模型在渠江流域的应用研究

为了进一步探究分布式水文模型(DDRM)的适用性,基于渠江流域的数字高程模型(DEM),提取了研究区域的边界、河网水系,计算了研究区域的地形指数;然后采用基于DEM的DDRM模型进行流域降雨-径流过程模拟,并用确定性系数、洪峰相对误差、峰现时间误差等对DDRM模型的模拟精度进行了评价;同时,对比分析了不同DEM栅格分辨率(1 km和2 km)对模型模拟精度的影响。结果表明:基于上述2种分辨率的DDRM模型在渠江流域降雨-径流模拟中均取得了较好的模拟效果,其中基于1 km栅格的DDRM模拟精度略优于2 km栅格。DDRM模型结构简单,参数较少、物理过程明确,而且能够模拟流域土壤含水量和径流量的空间分布,可为缺资料地区水文模拟提供一种新的方法。     

适用于Hadoop云平台的洪水淹没分析并行算法

数字高程模型(Digital Elevation Model, DEM)特殊的数据结构有利于数据在计算机中进行计算,但其数据量大,受计算机性能的限制,无法满足大规模洪水淹没分析的需要。针对单机计算能力有限以及有源淹没分析递归算法占用计算机资源较多的问题,提出了一种利用Hadoop云平台进行洪水淹没分析的并行算法。该算法对研究区域DEM按规则格网的思想进行划分,将大规模的研究区分割成多个规则的小区域。在指定水位下,对每个小区域进行数据压缩,计算每个栅格单元的淹没值,并将同一行中相连的淹没栅格序列记为淹没块,每个淹没块包含该栅格序列所属的区块号、行号、起始列号、终止列号以及淹没标记;当不同行或不同区域中的淹没块相互连通时,需对其淹没标记进行一致性设计。最终,根据淹没点所在淹没块的淹没标记提取出淹没点所在的淹没区,即为实际的淹没范围。实验结果表明,在指定淹没水位下,通过该算法可以快速提取淹没范围以及实现淹没水深的计算。     

基于图遍历的计算DEM数据洪水淹没范围的算法

针对数字高程模型(digital elevation model,DEM)大区域数据量大,而常用的洪水淹没算法(如种子蔓延法)不适用于对DEM的分块计算和分次存储,且用时和耗内存大,设计了一种使用图遍历来有效计算洪水淹没范围的算法,对DEM数据分条带读入计算机内存,然后采用块码压缩方式将潜在淹没区域压缩成块存入磁盘,使用广度优先搜索的图遍历方法读取数据。该算法设计逻辑清晰、实用性强且运算效率高,适用于大范围地形复杂的淹没区域。选取北京市、四川省的DEM数据进行实验,实验结果验证了该算法满足计算快速、占用内存少的要求。     

低山丘陵区多源数字高程模型误差分析北大核心CSCD

作为多学科交叉与渗透产物的数字高程模型(DEM)已在诸多学科和领域及实际应用中发挥了重要作用,但目前能够免费获取的高分辨全球DEM在不同区域仍存在很大的不确定性,应用之前进行质量评估至关重要。以烟台市为实验区,以大比例尺地形图(1∶10 000)生成的DEM为参照,结合坡度、坡向和土地覆被类型等地学因子,定量分析了目前广泛应用的两个版本ASTER GDEM(先进星载热辐射和反射辐射计全球数字高程模型)ASTETR 1和ASTER 2及不同空间分辨率SRTM DEM(航天飞机雷达地形测绘任务)(SRTM 1:～30m和SRTM 3:～90m)在低山丘陵区高程、坡度及坡向误差。结果表明:在研究区域内,ASTER 1、ASTER 2、SRTM 3、SRTM 1总体高程均方根误差分别为8.7m、6.3m、3.7m和2.9m。ASTER与SRTM的高程精度不同程度地受坡度、坡向以及土地覆被类型等地学因子的影响,DEM误差随坡度增加而增大,其中SRTM 3精度对该因子最敏感。尽管坡向对DEM精度影响不明显(4种DEM在不同坡向上的均方根误差波动范围均不超过2m),但是不同土地覆被类型下这4种DEM精度差异显著。此外,分析4种DEM提取的坡度可知,SRTM 1的均方根坡度误差最低(2.5°)、ASTER 1与ASTER 2的坡度的均方根误差大致相同(3.6°、3.9°)、SRTM 3的坡度均方根误差最高(4.3°)。坡向的精度SRTM 1最高,ASTER 1与ASTER 2次之,SRTM 3最低。研究结果对我国低山丘陵区ASTER GDEM与SRTM DEM的应用与精度评估具有一定的借鉴作用。     

规则网格数字高程模型中基于距离与坡度的路径规划算法

针对A^*算法在数字高程模型（DEM）路径规划中的低效问题,提出一种基于距离与坡度的改进A^*寻路算法。该算法面向规则网格DEM,以距离和坡度作为路径搜索评估指标,设计新的评价函数,并以地表障碍评判路径的可通行性。在寻路过程中,根据实际场景DEM数据计算相匹配的参数,使得改进算法能自适应不同场景下DEM数据分辨率的变化;采用动态权值调整完备性函数和启发性函数对评价结果的影响,优化路径选择。仿真测试结果表明,改进算法能够通过参数调整适应DEM分辨率的变化,搜索出优化的路径,降低搜索时间,提高搜索效率。     

The ANNS approach to DEM reconstruction

This research has 6 fundamental aims: (i) to present a modified version of Taylor's interpolation, one that is more effective and faster than the original; (ii) outline the capability of artificial neural networks (ANNs) to perform an optimal functional approximation of the digital elevation model reconstruction from a satellite map, using a small and independent sample of Global Positioning System observations; (iii) demonstrate experimentally how ANNs outperform the traditional and most used algorithm for the height interpolation (Taylor's interpolation); (iv) introduce a new ANN, the Conic Net, able to outperform the results of the classic and more known multilayer perceptron; (v) determine that Conic Nets, even when using Taylor's modified interpolation as input features, are able to optimally approximate the heights with one order of magnitude more than the original satellite map; and (vi) make evident the possibility to interpolate the DEM heights through an ANN, which learns a data set of known points.     

基于DEM和GIS的流域水文信息提取——以巴中市为例

为了探讨基于DEM和GIS的流域水文信息提取过程中阈值确定的有关问题,应用ArcGIS中的Hydrology水文分析工具,对巴中市水域的水文信息提取进行了研究。研究结果表明:1汇流累积量与河网密度、流域面积满足二阶导数关系,利用导数关系能够有效确定河网提取阈值。2阈值对河网信息提取具有较大的影响,阈值越小,河网越稠密。当阈值达到8500时,提取的河网密度和面积基本趋于稳定且与实际水系基本符合。3实际地形特征、原始DEM数据可能存在的误差以及其他人为因素等都会对水文提取结果产生影响。