The International Maritime Organization has developed the second-generation intact stability criteria. Thus, damage stability criteria can be established in the future. In order to identity the capsizing probability of damaged ship under dead ship condition, this paper investigates two methods that can be used to research the capsizing probability in time domain, which mainly focus on the nonlinear righting lever GZ curve solution. One method subjects the influence of damaged tanks on the hull shape down to the wind and wave, and the other method is consistent with the real-time calculation of the GZ curve. On the basis of one degree of freedom rolling equation, the solution is Monte Carlo method, and a damaged fishery bureau vessel is taken as a sample ship. In addition, the results of the time-domain capsizing probability under different loading conditions are compared and analyzed. The relation of GM and heeling angle with the capsizing probability is investigated, and its possible reason is analyzed. On the basis of combining the time-domain flooding process with the capsizing probability calculation, this research aims to lay the foundation for the study of capsizing probability in time domain under dead ship condition, as well as provide technical support for capsizing mechanism of dead ship stability and damage stability criteria establishment in waves. 相似文献
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