Pore structure characteristics of rock are a great concern for researchers and practitioners in rock mechanics and rock engineering fields. In this study, mercury intrusion porosimetry（MIP） was used to measure pore ...Pore structure characteristics of rock are a great concern for researchers and practitioners in rock mechanics and rock engineering fields. In this study, mercury intrusion porosimetry（MIP） was used to measure pore size distribution, as well as several important index parameters of pore structure, for seven common types of deep sedimentary rocks with a total of fifty rock samples. Results show a similar pore size distribution pattern of the rock samples in the same lithological group, but remarkable differences among different lithological groups. Among seven investigated rock types, mudstone has the smallest porosity of 3.37%, while conglomerate has the largest value of 18.8%. It is also found that the porosity of rock types with finer grain size is lower than those with coarser grain size. Meanwhile, a comparison of frequency distribution at ten intervals of pore-throat diameter among seven types of sedimentary rocks reveals that different rock types have different dominant pore-size ranges. Furthermore, permeability of the investigated sedimentary rock samples was derived based on MIP data using reported theoretical equations. Among seven rock types, mudstone has the lowest averaged permeability（3.64×10^（-6） mD） while conglomerate has the highest one（8.59×10^（-4） mD）. From mudstone to conglomerate, rock permeability increases with an increase of grain size, with only an exception of siltstone which has a relatively larger porosity value. Finally, regression analysis show that there is a good fitting（R^2=0.95） between permeability and porosity which could be easily used to derive reliable permeability values of similar kinds of engineering rocks.展开更多
A new penalty-free neural network method,PFNN-2,is presented for solving partial differential equations,which is a subsequent improvement of our previously proposed PFNN method[1].PFNN-2 inherits all advantages of PFN...A new penalty-free neural network method,PFNN-2,is presented for solving partial differential equations,which is a subsequent improvement of our previously proposed PFNN method[1].PFNN-2 inherits all advantages of PFNN in handling the smoothness constraints and essential boundary conditions of self-adjoint problems with complex geometries,and extends the application to a broader range of non-self-adjoint time-dependent differential equations.In addition,PFNN-2 introduces an overlapping domain decomposition strategy to substantially improve the training efficiency without sacrificing accuracy.Experiments results on a series of partial differential equations are reported,which demonstrate that PFNN-2 can outperform state-of-the-art neural network methods in various aspects such as numerical accuracy,convergence speed,and parallel scalability.展开更多
基金supported by the National Natural Science Foundation of China(Nos.41502264,51134005)the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20130023120016)the Fundamental Research Funds for the Central Universities of China(No.2010QL07)
文摘Pore structure characteristics of rock are a great concern for researchers and practitioners in rock mechanics and rock engineering fields. In this study, mercury intrusion porosimetry（MIP） was used to measure pore size distribution, as well as several important index parameters of pore structure, for seven common types of deep sedimentary rocks with a total of fifty rock samples. Results show a similar pore size distribution pattern of the rock samples in the same lithological group, but remarkable differences among different lithological groups. Among seven investigated rock types, mudstone has the smallest porosity of 3.37%, while conglomerate has the largest value of 18.8%. It is also found that the porosity of rock types with finer grain size is lower than those with coarser grain size. Meanwhile, a comparison of frequency distribution at ten intervals of pore-throat diameter among seven types of sedimentary rocks reveals that different rock types have different dominant pore-size ranges. Furthermore, permeability of the investigated sedimentary rock samples was derived based on MIP data using reported theoretical equations. Among seven rock types, mudstone has the lowest averaged permeability（3.64×10^（-6） mD） while conglomerate has the highest one（8.59×10^（-4） mD）. From mudstone to conglomerate, rock permeability increases with an increase of grain size, with only an exception of siltstone which has a relatively larger porosity value. Finally, regression analysis show that there is a good fitting（R^2=0.95） between permeability and porosity which could be easily used to derive reliable permeability values of similar kinds of engineering rocks.
基金supported in part by National Natural Science Foundation of China(grants#12131002 and#12288101)Huawei Technologies Co.,Ltd.
文摘A new penalty-free neural network method,PFNN-2,is presented for solving partial differential equations,which is a subsequent improvement of our previously proposed PFNN method[1].PFNN-2 inherits all advantages of PFNN in handling the smoothness constraints and essential boundary conditions of self-adjoint problems with complex geometries,and extends the application to a broader range of non-self-adjoint time-dependent differential equations.In addition,PFNN-2 introduces an overlapping domain decomposition strategy to substantially improve the training efficiency without sacrificing accuracy.Experiments results on a series of partial differential equations are reported,which demonstrate that PFNN-2 can outperform state-of-the-art neural network methods in various aspects such as numerical accuracy,convergence speed,and parallel scalability.
