The stability control of surrounding rock for large or super-large section chamber is a difficult technical problem in deep mining condition.Based on the in-site geological conditions of Longgu coal mine,this paper us...The stability control of surrounding rock for large or super-large section chamber is a difficult technical problem in deep mining condition.Based on the in-site geological conditions of Longgu coal mine,this paper used the dynamic module of FLAC3D to study the response characteristics of deep super-large section chamber under dynamic and static combined loading condition.Results showed that under the static loading condition,the maximum vertical stress,deformation and failure range are large,where the stress concentration coefficient is 1.64.The maximum roof-to-floor and two-sides deformations are 54.6 mm and 53.1 mm,respectively.Then,under the dynamic and static combined loading condition:(1)The influence of dynamic load frequency on the two-sides is more obvious;(2)The dynamic load amplitude has the greatest influence on the stress concentration degree,and the plastic failure tends to develop to the deeper;(3)With the dynamic load source distance increase,the response of surrounding rock is gradually attenuated.On this basis,empirical equations for each dynamic load conditions were obtained by using regression analysis method,and all correlation coefficients are greater than 0.99.This research provided reference for the supporting design of deep super-large section chamber under same or similar conditions.展开更多
We studied the dynamic fracture mechanical behavior of rock under different impact rates. The fracture experiment was a three-point bending beam subjected to different impact loads monitored using the reflected causti...We studied the dynamic fracture mechanical behavior of rock under different impact rates. The fracture experiment was a three-point bending beam subjected to different impact loads monitored using the reflected caustics method. The mechanical parameters for fracture of the three-poim bending beam specimen under impact load are analyzed. The mechanism of crack propagation is discussed. Experimental results show that the dynamic stress intensity factor increases before crack initiation. When the dynamic stress intensity factor reaches its maximum value the crack starts to develop. After crack initiation the dynamic stress intensity factor decreases rapidly and oscillates. As the impact rate increases the cracks initiate earlier, the maximum value of crack growth velocity becomes smaller and the values of dynamic stress intensity factor also vary less during crack propagation. The results provide a theoretical basis for the study of rock dynamic fracture.展开更多
To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which s...To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which supports both traditional communication and MEC services.However,this kind of intensive computing problem is a high dimensional NP hard problem,and some machine learning methods do not have a good effect on solving this problem.In this paper,the Markov decision process model is established to find the excellent task offloading scheme,which maximizes the long-term utility performance,so as to make the best offloading decision according to the queue state,energy queue state and channel quality between mobile users and BS.In order to explore the curse of high dimension in state space,a candidate network is proposed based on edge computing optimize offloading(ECOO)algorithm with the application of deep deterministic policy gradient algorithm.Through simulation experiments,it is proved that the ECOO algorithm is superior to some deep reinforcement learning algorithms in terms of energy consumption and time delay.So the ECOO is good at dealing with high dimensional problems.展开更多
基金Project(2018YFC0604703)supported by the National Key R&D Program of ChinaProjects(51804181,51874190)supported by the National Natural Science Foundation of China+3 种基金Project(ZR2018QEE002)supported by the Shandong Province Natural Science Fund,ChinaProject(ZR2018ZA0603)supported by the Major Program of Shandong Province Natural Science Foundation,ChinaProject(2019GSF116003)supported by the Key R&D Project of Shandong Province,ChinaProject(SDKDYC190234)supported by the Shandong University of Science and Technology,Graduate Student Technology Innovation Project,China。
文摘The stability control of surrounding rock for large or super-large section chamber is a difficult technical problem in deep mining condition.Based on the in-site geological conditions of Longgu coal mine,this paper used the dynamic module of FLAC3D to study the response characteristics of deep super-large section chamber under dynamic and static combined loading condition.Results showed that under the static loading condition,the maximum vertical stress,deformation and failure range are large,where the stress concentration coefficient is 1.64.The maximum roof-to-floor and two-sides deformations are 54.6 mm and 53.1 mm,respectively.Then,under the dynamic and static combined loading condition:(1)The influence of dynamic load frequency on the two-sides is more obvious;(2)The dynamic load amplitude has the greatest influence on the stress concentration degree,and the plastic failure tends to develop to the deeper;(3)With the dynamic load source distance increase,the response of surrounding rock is gradually attenuated.On this basis,empirical equations for each dynamic load conditions were obtained by using regression analysis method,and all correlation coefficients are greater than 0.99.This research provided reference for the supporting design of deep super-large section chamber under same or similar conditions.
基金the support of the National Natural Science Foundation of China (Grant No.50774086 and 50874109)
文摘We studied the dynamic fracture mechanical behavior of rock under different impact rates. The fracture experiment was a three-point bending beam subjected to different impact loads monitored using the reflected caustics method. The mechanical parameters for fracture of the three-poim bending beam specimen under impact load are analyzed. The mechanism of crack propagation is discussed. Experimental results show that the dynamic stress intensity factor increases before crack initiation. When the dynamic stress intensity factor reaches its maximum value the crack starts to develop. After crack initiation the dynamic stress intensity factor decreases rapidly and oscillates. As the impact rate increases the cracks initiate earlier, the maximum value of crack growth velocity becomes smaller and the values of dynamic stress intensity factor also vary less during crack propagation. The results provide a theoretical basis for the study of rock dynamic fracture.
基金National Natural Science Foundation of China(No.11461038)Science and Technology Support Program of Gansu Province(No.144NKCA040)。
文摘To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which supports both traditional communication and MEC services.However,this kind of intensive computing problem is a high dimensional NP hard problem,and some machine learning methods do not have a good effect on solving this problem.In this paper,the Markov decision process model is established to find the excellent task offloading scheme,which maximizes the long-term utility performance,so as to make the best offloading decision according to the queue state,energy queue state and channel quality between mobile users and BS.In order to explore the curse of high dimension in state space,a candidate network is proposed based on edge computing optimize offloading(ECOO)algorithm with the application of deep deterministic policy gradient algorithm.Through simulation experiments,it is proved that the ECOO algorithm is superior to some deep reinforcement learning algorithms in terms of energy consumption and time delay.So the ECOO is good at dealing with high dimensional problems.
