The transfer and evolution of stress among rock blocks directly change the void ratios of crushed rock masses and affect the flow of methane in coal mine gobs. In this study, a Lagrange framework and a discrete element method, along with the soft-sphere model and EDEM numerical software, were used. The compaction processes of rock blocks with diameters of 0.6, 0.8, and 1.0 m were simulated with the degrees of compression set at 0%, 5%, 10%, 15%, 20%, and 25%. This study examines the influence of stress on void ratios of compacted crushed rock masses in coal mine gobs. The results showed that stress was mainly transmitted downward through strong force chains. As the degree of compression increased, the strong force chains extended downward, which resulted in the stress at the upper rock mass to become significantly higher than that at the lower rock mass. It was determined that under different degrees of compression, the rock mass of coal mine gobs could be divided, from the bottom to the top, into a lower insufficient compression zone (ICZ) and an upper sufficient compression zone (SCZ). From bottom to top, the void ratios in the ICZ sharply decreased and those in the SCZ slowly decreased. Void ratios in the ICZ were 1.2–1.7 times higher than those in the SCZ.
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
Journal of Geographical Sciences - The border areas of the Tibetan Plateau and the neighboring mountainous areas have a high incidence of earthquakes with a magnitude greater than Ms 5.0, as well... 相似文献
The ecological series of soil animals under the broad-leaved and pine mixed forest in Darlidai Mountainwas studied. Seven sample plots were selected according to different altitude gradients, which belong to different vegeta-tion types. By investigating and analyzing soil animals in every sample plot it is found that there are 45 groups and 1956individuals, which axe involved in 3 phylums, 7 classes, 16 orders, respectively. The altitude is a key factor which af-fects ecological series of soil animals. Both the groups and individuals of soil animals increase with altitude increasingunder certain conditions, which contrastes with ordinary cases, resulting from special micro-climate in studied area. Thegroups and individuls of soil animals are the most under the broad-leaved and pine forest on the top of the mountain, andthe least under Picea-Abies forest in the foot of the mountain. 相似文献