The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not o...The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.展开更多
After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtuali...After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.展开更多
This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics （CFD） approach was used as modeling method, an...This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics （CFD） approach was used as modeling method, and turbulent intensity and fluid velocity were applied as system responses to predict the over- flow cut size variations. These investigations showed that cut size would decrease by increasing diameter and height of the separation column and cone section depth, due to the decrease of turbulent intensity and fluid velocity. As the size of discharge gate increases, the overflow cut-size would decrease due to freely fluid stream out of the column. The overflow cut-size was significantly increased in downward fed classifier compared to that fed by upward fluid stream. In addition, reforming the shape of angular overflow outlet＇s weir into the curved form prevented stream inside returning and consequently unselec- tire cut-size decreasing.展开更多
Metal organic chenlical vapor deposition （AIOCVD） growth systems arc one of the. main types of equipment used for growing single crystal materials, such as GaN. To obtain fihn epitaxial materials with uniform perfor...Metal organic chenlical vapor deposition （AIOCVD） growth systems arc one of the. main types of equipment used for growing single crystal materials, such as GaN. To obtain fihn epitaxial materials with uniform performanee, the flow field and ternperature field in a GaN-MOCVD reactor are investigated by modeling and simulating. To make the simulation results more consistent with the actual situation, the gases in the reactor are considered to be compressible, making it possible to investigate the distributions of gas density and pressure in the reactor. The computational fluid dynamics method is used to stud,v the effects of inlet gas flow velocity, pressure in the reactor, rotational speed of graphite susceptor, and gases used in the growth, which has great guiding~ significance for the growth of GaN fihn materials.展开更多
With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this pap...With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.展开更多
Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into accou...Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.展开更多
Extremely low-frequency magnetic field is widely used as a noninvasive stimulation method in clinical practice and basic research. Electrical field induced from magnetic pulse can decrease or increase neuronal electri...Extremely low-frequency magnetic field is widely used as a noninvasive stimulation method in clinical practice and basic research. Electrical field induced from magnetic pulse can decrease or increase neuronal electrical activity. However, the cellular mechanism underlying the effects of magnetic field is not clear from experimental data. Recent studies have demonstrated that "non-neuronal" cells, especially astrocytes, may be the potential effector for transcranial magnetic stimulation(TMS). In the present study, we implemented a neural–astrocyte microcircuit computational model based on hippocampal architecture to investigate the biological effects of different magnetic field frequencies on cells. The purpose of the present study is to elucidate the main influencing factors of MS to allow a better understanding of its mechanisms.Our model reproduced the basic characteristics of the neuron and astrocyte response to different magnetic stimulation. The results predict that interneurons with lower firing thresholds were more active in magnetic fields by contrast to pyramidal neurons. And the synaptic coupling strength between the connected neurons may be one of the critical factor to affect the effect of magnetic field on cells. In addition, the simulations show that astrocytes can decrease or increase slow inward currents(SICs) to finely tune neuronal excitation, which suggests their key role in excitatory–inhibitory balance. The interaction between neurons and astrocytes may represent a novel target for effective therapeutic strategies involving magnetic stimulation.展开更多
A computational model has been developed for the simulation of pedestrian level wind environment around tall buildings by coupling the numerical simulation of the full scale site and meteorological station materials...A computational model has been developed for the simulation of pedestrian level wind environment around tall buildings by coupling the numerical simulation of the full scale site and meteorological station materials. In the first step, the hybrid/mixed finite element method is employed to solve the two dimensional Navier Stokes equation for the flow field around tall buildings, in view of the influence of fluctuating wind, the flow field is revised with the effective wind velocity. The velocity ratio is defined in order to relate numerical wind velocity to oncoming reference wind velocity. In the second step, the frequency occurred discomfort wind velocity as a suitable criterion is calculated by use of the coupling between the numerical wind velocity and the wind velocity at the nearest meteorological station. The prediction accuracy of the wind environment simulation by use of the computation model will be discussed. Using the available wind data at the nearest meteorological station as well as the established criteria of wind discomfort, the frequency of wind discomfort can be predicted. A numerical example is given to illustrate the application of the proposed method.展开更多
With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these...With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.展开更多
With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a c...With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.展开更多
With ongoing development of oil exploration and techniques,there is a significant need for improved well control strategies and formation pressure prediction methods.In this paper,a gas-liquid transient drift flow mod...With ongoing development of oil exploration and techniques,there is a significant need for improved well control strategies and formation pressure prediction methods.In this paper,a gas-liquid transient drift flow model was established according to the gas-liquid two-phase flow characteristics during the gas kick.A Roe scheme was used for numerical calculation based on the finite volume method.The changes of bottom-hole pressure,casing pressure,the development law of cross-sectional gas holdup,and gas velocity,along with the vertical well depth,were analyzed through simulation examples.The time-series characteristics of mud pit gain were obtained by adjusting the formation parameter.The complex nonlinear mapping relationship between the formation parameters and the mud pit gain was established.The long short-term memory network(LSTM)of deep learning was used to obtain a formation pressure inversion when the blowout is out of control and the well cannot be shut-in.Experimental data from a well were used to verify the gas-liquid two-phase transient drift flow model based on the finite volume method,demonstrating that this method is reliable,with greatly improved prediction accuracy.This approach provides theoretical support for the early monitoring of gas kick during drilling,and for well-killing design and construction after uncontrolled blowout.展开更多
This study aims to clarify the factors influencing oil recovery of surfactant-polymer(SP)flooding and to establish a quantitative calculation model of oil recovery during different displacement stages from water flood...This study aims to clarify the factors influencing oil recovery of surfactant-polymer(SP)flooding and to establish a quantitative calculation model of oil recovery during different displacement stages from water flooding to SP flooding.The conglomerate reservoir of the Badaowan Formation in the seventh block of the Karamay Oilfield is selected as the research object to reveal the start-up mechanism of residual oil and determine the controlling factors of oil recovery through SP flooding experiments of natural cores and microetching models.The experimental results are used to identify four types of residual oil after water flooding in this conglomerate reservoir with a complex pore structure:oil droplets retained in pore throats by capillary forces,oil cluster trapped at the junction of pores and throats,oil film on the rock surface,isolated oil in dead-ends of flow channel.For the four types of residual oil identified,the SP solution can enhance oil recovery by enlarging the sweep volume and improving the oil displacement efficiency.First,the viscosity-increasing effect of the polymer can effectively reduce the permeability of the displacement liquid phase,change the oil-water mobility ratio,and increase the water absorption.Furthermore,the stronger the shear drag force of the SP solution,the more the crude oil in a porous medium is displaced.Second,the surfactant can change the rock wettability and reduce the absorption capacity of residual oil by lowering interfacial tension.At the same time,the emulsification further increases the viscosity of the SP solution,and the residual oil is recovered effectively under the combined effect of the above two factors.For the four start-up mechanisms of residual oil identified after water flooding,enlarging the sweep volume and improving the oil displacement efficiency are interdependent,but their contribution to enhanced oil recovery are different.The SP flooding system primarily enlarges the sweep volume by increasing viscosity of solution to start two kinds of residual oil such as oil droplet retained in pore throats and isolated oil in dead-ends of flow channel,and primarily improves the oil displacement efficiency by lowing interfacial tension of oil phase to start two kinds of residual oil such as oil cluster trapped at the junction of pores and oil film on the rock surface.On this basis,the experimental results of the oil displacement from seven natural cores show that the pore structure of the reservoir is the main factor influencing water flooding recovery,while the physical properties and original oil saturation have relatively little influence.The main factor influencing SP flooding recovery is the physical and chemical properties of the solution itself,which primarily control the interfacial tension and solution viscosity in the reservoir.The residual oil saturation after water flooding is the material basis of SP flooding,and it is the second-most dominant factor controlling oil recovery.Combined with the analysis results of the influencing factors and reservoir parameters,the water flooding recovery index and SP flooding recovery index are defined to further establish quantitative calculation models of oil recovery under different displacement modes.The average relative errors of the two models are 4.4%and 2.5%,respectively;thus,they can accurately predict the oil recovery of different displacement stages and the ultimate reservoir oil recovery.展开更多
Separation issue is one of the most important problems about cloud computing security. Tenants should be separated from each other based on cloud infrastructure and different users from one tenant should be separated ...Separation issue is one of the most important problems about cloud computing security. Tenants should be separated from each other based on cloud infrastructure and different users from one tenant should be separated from each other with the constraint of security policies. Learning from the notion of trusted cloud computing and trustworthiness in cloud, in this paper, a multi-level authorization separation model is formally described, and a series of rules are proposed to summarize the separation property of this model. The correctness of the rules is proved. Furthermore, based on this model, a tenant separation mechanism is deployed in a real world mixed-critical information system. Performance benchmarks have shown the availability and efficiency of this mechanism.展开更多
The effect of the moving speed of permanent magnet （PM） on levitation force between PM and high temperature superconducting （HTS） bulk is analyzed and described in the PM-HTS levitation system. The PM vibration ch...The effect of the moving speed of permanent magnet （PM） on levitation force between PM and high temperature superconducting （HTS） bulk is analyzed and described in the PM-HTS levitation system. The PM vibration characteristic in the PM-HTS system is investigated. The PM may collide with the HTS in vibration if the amplitude and frequency of driving force satisfy the relationship Pmin=Afn. When the load of the system is below a threshold, the minimal collision amplitude of the driving force increases with the load increasing, however, it sharply drops to zero when the load exceeds the threshold. With the increase of the initial height of the PM, the threshold load increases, but the minimal driving force which causes a collision between PM and HTS decreases.展开更多
文摘The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.
文摘After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.
基金financially supported by INVENTIVE~ Mineral Processing Research Center of Iran
文摘This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics （CFD） approach was used as modeling method, and turbulent intensity and fluid velocity were applied as system responses to predict the over- flow cut size variations. These investigations showed that cut size would decrease by increasing diameter and height of the separation column and cone section depth, due to the decrease of turbulent intensity and fluid velocity. As the size of discharge gate increases, the overflow cut-size would decrease due to freely fluid stream out of the column. The overflow cut-size was significantly increased in downward fed classifier compared to that fed by upward fluid stream. In addition, reforming the shape of angular overflow outlet＇s weir into the curved form prevented stream inside returning and consequently unselec- tire cut-size decreasing.
基金Supported by the National Key R&D Program of China under Grant No 2016YFB0400104
文摘Metal organic chenlical vapor deposition （AIOCVD） growth systems arc one of the. main types of equipment used for growing single crystal materials, such as GaN. To obtain fihn epitaxial materials with uniform performanee, the flow field and ternperature field in a GaN-MOCVD reactor are investigated by modeling and simulating. To make the simulation results more consistent with the actual situation, the gases in the reactor are considered to be compressible, making it possible to investigate the distributions of gas density and pressure in the reactor. The computational fluid dynamics method is used to stud,v the effects of inlet gas flow velocity, pressure in the reactor, rotational speed of graphite susceptor, and gases used in the growth, which has great guiding~ significance for the growth of GaN fihn materials.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2013RC0114111 Project of China under Grant No.B08004
文摘With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.
基金Project supported by the National Natural Science Foundation of China(Grant No.61332003)High Performance Computing Laboratory,China(Grant No.201501-02)
文摘Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.
基金supported by the National Natural Science Foundation of China (Grant No. 61673158)the Youth Talent Support Program of Hebei Province,China(Grant No. BJ2019044)。
文摘Extremely low-frequency magnetic field is widely used as a noninvasive stimulation method in clinical practice and basic research. Electrical field induced from magnetic pulse can decrease or increase neuronal electrical activity. However, the cellular mechanism underlying the effects of magnetic field is not clear from experimental data. Recent studies have demonstrated that "non-neuronal" cells, especially astrocytes, may be the potential effector for transcranial magnetic stimulation(TMS). In the present study, we implemented a neural–astrocyte microcircuit computational model based on hippocampal architecture to investigate the biological effects of different magnetic field frequencies on cells. The purpose of the present study is to elucidate the main influencing factors of MS to allow a better understanding of its mechanisms.Our model reproduced the basic characteristics of the neuron and astrocyte response to different magnetic stimulation. The results predict that interneurons with lower firing thresholds were more active in magnetic fields by contrast to pyramidal neurons. And the synaptic coupling strength between the connected neurons may be one of the critical factor to affect the effect of magnetic field on cells. In addition, the simulations show that astrocytes can decrease or increase slow inward currents(SICs) to finely tune neuronal excitation, which suggests their key role in excitatory–inhibitory balance. The interaction between neurons and astrocytes may represent a novel target for effective therapeutic strategies involving magnetic stimulation.
文摘A computational model has been developed for the simulation of pedestrian level wind environment around tall buildings by coupling the numerical simulation of the full scale site and meteorological station materials. In the first step, the hybrid/mixed finite element method is employed to solve the two dimensional Navier Stokes equation for the flow field around tall buildings, in view of the influence of fluctuating wind, the flow field is revised with the effective wind velocity. The velocity ratio is defined in order to relate numerical wind velocity to oncoming reference wind velocity. In the second step, the frequency occurred discomfort wind velocity as a suitable criterion is calculated by use of the coupling between the numerical wind velocity and the wind velocity at the nearest meteorological station. The prediction accuracy of the wind environment simulation by use of the computation model will be discussed. Using the available wind data at the nearest meteorological station as well as the established criteria of wind discomfort, the frequency of wind discomfort can be predicted. A numerical example is given to illustrate the application of the proposed method.
基金supported by the National Science Foundation of China under Grant 62271062 and 62071063by the Zhijiang Laboratory Open Project Fund 2020LCOAB01。
文摘With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.
基金express their gratitude to the Higher Institution Centre of Excellence (HICoE) fund under the project code (JPT.S(BPKI)2000/016/018/015JId.4(21)/2022002HICOE)Universiti Tenaga Nasional (UNITEN) for funding the research through the (J510050002–IC–6 BOLDREFRESH2025)Akaun Amanah Industri Bekalan Elektrik (AAIBE) Chair of Renewable Energy grant,and NEC Energy Transition Grant (202203003ETG)。
文摘With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.
基金financially supported by the National Natural Science Foundation of China(Grant No.51974090,51474073)
文摘With ongoing development of oil exploration and techniques,there is a significant need for improved well control strategies and formation pressure prediction methods.In this paper,a gas-liquid transient drift flow model was established according to the gas-liquid two-phase flow characteristics during the gas kick.A Roe scheme was used for numerical calculation based on the finite volume method.The changes of bottom-hole pressure,casing pressure,the development law of cross-sectional gas holdup,and gas velocity,along with the vertical well depth,were analyzed through simulation examples.The time-series characteristics of mud pit gain were obtained by adjusting the formation parameter.The complex nonlinear mapping relationship between the formation parameters and the mud pit gain was established.The long short-term memory network(LSTM)of deep learning was used to obtain a formation pressure inversion when the blowout is out of control and the well cannot be shut-in.Experimental data from a well were used to verify the gas-liquid two-phase transient drift flow model based on the finite volume method,demonstrating that this method is reliable,with greatly improved prediction accuracy.This approach provides theoretical support for the early monitoring of gas kick during drilling,and for well-killing design and construction after uncontrolled blowout.
基金supported by the National Natural Science Foundation of China(No.41902141)the Fundamental Research Fund for the Central Universities(No.E1E40403)the PetroChina Innovation Foundation(No.2018D-5007-0103)
文摘This study aims to clarify the factors influencing oil recovery of surfactant-polymer(SP)flooding and to establish a quantitative calculation model of oil recovery during different displacement stages from water flooding to SP flooding.The conglomerate reservoir of the Badaowan Formation in the seventh block of the Karamay Oilfield is selected as the research object to reveal the start-up mechanism of residual oil and determine the controlling factors of oil recovery through SP flooding experiments of natural cores and microetching models.The experimental results are used to identify four types of residual oil after water flooding in this conglomerate reservoir with a complex pore structure:oil droplets retained in pore throats by capillary forces,oil cluster trapped at the junction of pores and throats,oil film on the rock surface,isolated oil in dead-ends of flow channel.For the four types of residual oil identified,the SP solution can enhance oil recovery by enlarging the sweep volume and improving the oil displacement efficiency.First,the viscosity-increasing effect of the polymer can effectively reduce the permeability of the displacement liquid phase,change the oil-water mobility ratio,and increase the water absorption.Furthermore,the stronger the shear drag force of the SP solution,the more the crude oil in a porous medium is displaced.Second,the surfactant can change the rock wettability and reduce the absorption capacity of residual oil by lowering interfacial tension.At the same time,the emulsification further increases the viscosity of the SP solution,and the residual oil is recovered effectively under the combined effect of the above two factors.For the four start-up mechanisms of residual oil identified after water flooding,enlarging the sweep volume and improving the oil displacement efficiency are interdependent,but their contribution to enhanced oil recovery are different.The SP flooding system primarily enlarges the sweep volume by increasing viscosity of solution to start two kinds of residual oil such as oil droplet retained in pore throats and isolated oil in dead-ends of flow channel,and primarily improves the oil displacement efficiency by lowing interfacial tension of oil phase to start two kinds of residual oil such as oil cluster trapped at the junction of pores and oil film on the rock surface.On this basis,the experimental results of the oil displacement from seven natural cores show that the pore structure of the reservoir is the main factor influencing water flooding recovery,while the physical properties and original oil saturation have relatively little influence.The main factor influencing SP flooding recovery is the physical and chemical properties of the solution itself,which primarily control the interfacial tension and solution viscosity in the reservoir.The residual oil saturation after water flooding is the material basis of SP flooding,and it is the second-most dominant factor controlling oil recovery.Combined with the analysis results of the influencing factors and reservoir parameters,the water flooding recovery index and SP flooding recovery index are defined to further establish quantitative calculation models of oil recovery under different displacement modes.The average relative errors of the two models are 4.4%and 2.5%,respectively;thus,they can accurately predict the oil recovery of different displacement stages and the ultimate reservoir oil recovery.
基金supported by the Fundamental Research funds for the central Universities of China (No. K15JB00190)the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120009120010)the Program for Innovative Research Team in University of Ministry of Education of China (IRT201206)
文摘Separation issue is one of the most important problems about cloud computing security. Tenants should be separated from each other based on cloud infrastructure and different users from one tenant should be separated from each other with the constraint of security policies. Learning from the notion of trusted cloud computing and trustworthiness in cloud, in this paper, a multi-level authorization separation model is formally described, and a series of rules are proposed to summarize the separation property of this model. The correctness of the rules is proved. Furthermore, based on this model, a tenant separation mechanism is deployed in a real world mixed-critical information system. Performance benchmarks have shown the availability and efficiency of this mechanism.
基金This work was supported by the National Science Foundation of China under Grant No. 50372052, 50588201the National Basic Research Program of China under Grant No. 2007CB616906+1 种基金the Program for Changjiang Scholars and Innovative Research Team in UniversityAustralian Research Council under Grant No. DP0559872, DP0881739.
文摘The effect of the moving speed of permanent magnet （PM） on levitation force between PM and high temperature superconducting （HTS） bulk is analyzed and described in the PM-HTS levitation system. The PM vibration characteristic in the PM-HTS system is investigated. The PM may collide with the HTS in vibration if the amplitude and frequency of driving force satisfy the relationship Pmin=Afn. When the load of the system is below a threshold, the minimal collision amplitude of the driving force increases with the load increasing, however, it sharply drops to zero when the load exceeds the threshold. With the increase of the initial height of the PM, the threshold load increases, but the minimal driving force which causes a collision between PM and HTS decreases.
