Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology...Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology remain largely unexplored.Leveraging the advantages of the finite-discrete element method(FDEM)for explicitly simulating fracture propagation and the strengths of the unifiedpipe model(UPM)for efficientlymodeling dual-permeability seepage,we propose a new hydromechanical(HM)coupling approach for modeling hydraulic fracturing.Validated against benchmark examples,the proposed FDEM-UPM model is further augmented by incorporating a Fourier-based methodology for reconstructing non-planar fractures,enabling quantitative analysis of hydraulic fracturing behavior within rough discrete fracture networks(DFNs).The FDEM-UPM model demonstrates computational advantages in accurately capturing transient hydraulic seepage phenomena,while the asynchronous time-stepping schemes between hydraulic and mechanical analyses substantially enhanced computational efficiencywithout compromising computational accuracy.Our results show that fracture morphology can affect both macroscopic fracture networks and microscopic interaction types between hydraulic fractures(HFs)and natural fractures(NFs).In an isotropic stress field,the initiation azimuth,propagation direction and microcracking mechanism are significantly influencedby fracture roughness.In an anisotropic stress field,HFs invariably propagate parallel to the direction of the maximum principal stress,reducing the overall complexity of the stimulated fracture networks.Additionally,stress concentration and perturbation attributed to fracture morphology tend to be compromised as the leak-off increases,while the breakdown and propagation pressures remain insensitive to fracture morphology.These findingsprovide new insights into the hydraulic fracturing mechanisms of fractured reservoirs containing complex rough DFNs.展开更多
Discrete fracture network(DFN)commonly existing in natural rock masses plays an important role in geological complexity which can influence rock fracturing behaviour during fluid injection.This paper simulated the hyd...Discrete fracture network(DFN)commonly existing in natural rock masses plays an important role in geological complexity which can influence rock fracturing behaviour during fluid injection.This paper simulated the hydraulic fracturing process in lab-scale coal samples with DFNs and the induced seismic activities by the discrete element method(DEM).The effects of DFNs on hydraulic fracturing,induced seismicity and elastic property changes have been concluded.Denser DFNs can comprehensively decrease the peak injection pressure and injection duration.The proportion of strong seismic events increases first and then decreases with increasing DFN density.In addition,the relative modulus of the rock mass is derived innovatively from breakdown pressure,breakdown fracture length and the related initiation time.Increasing DFN densities among large(35–60 degrees)and small(0–30 degrees)fracture dip angles show opposite evolution trends in relative modulus.The transitional point(dip angle)for the opposite trends is also proportionally affected by the friction angle of the rock mass.The modelling results have much practical meaning to infer the density and geometry of pre-existing fractures and the elastic property of rock mass in the field,simply based on the hydraulic fracturing and induced seismicity monitoring data.展开更多
Wellbore breakout is one of the critical issues in drilling due to the fact that the related problems result in additional costs and impact the drilling scheme severely.However,the majority of such wellbore breakout a...Wellbore breakout is one of the critical issues in drilling due to the fact that the related problems result in additional costs and impact the drilling scheme severely.However,the majority of such wellbore breakout analyses were based on continuum mechanics.In addition to failure in intact rocks,wellbore breakouts can also be initiated along natural discontinuities,e.g.weak planes and fractures.Furthermore,the conventional models in wellbore breakouts with uniform distribution fractures could not reflect the real drilling situation.This paper presents a fully coupled hydro-mechanical model of the SB-X well in the Tarim Basin,China for evaluating wellbore breakouts in heavily fractured rocks under anisotropic stress states using the distinct element method(DEM)and the discrete fracture network(DFN).The developed model was validated against caliper log measurement,and its stability study was carried out by stress and displacement analyses.A parametric study was performed to investigate the effects of the characteristics of fracture distribution(orientation and length)on borehole stability by sensitivity studies.Simulation results demonstrate that the increase of the standard deviation of orientation when the fracture direction aligns parallel or perpendicular to the principal stress direction aggravates borehole instability.Moreover,an elevation in the average fracture length causes the borehole failure to change from the direction of the minimum in-situ horizontal principal stress(i.e.the direction of wellbore breakouts)towards alternative directions,ultimately leading to the whole wellbore failure.These findings provide theoretical insights for predicting wellbore breakouts in heavily fractured rocks.展开更多
In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models...In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models,some existing discretized continuous neuron models,and discrete neural networks in simulating complex neural dynamics.It places particular emphasis on the importance of memristors in the composition of neural networks,especially their unique memory and nonlinear characteristics.The integration of memristors into discrete neural networks,including Hopfield networks and their fractional-order variants,cellular neural networks and discrete neuron models has enabled the study and construction of various neural models with memory.These models exhibit complex dynamic behaviors,including superchaotic attractors,hidden attractors,multistability,and synchronization transitions.Furthermore,the present paper undertakes an analysis of more complex dynamical properties,including synchronization,speckle patterns,and chimera states in discrete coupled neural networks.This research provides new theoretical foundations and potential applications in the fields of brain-inspired computing,artificial intelligence,image encryption,and biological modeling.展开更多
Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overa...Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overall direction of hydraulic fractures does not show a delineated shape parallel to the maximum principal stress orientation.A field-scale numerical model based on the distinct element method is set up to carry out a fully coupled hydromechanical simulation,with the explicit representation of natural fractures via the discrete fracture network(DFN)approach.The effects of injection parameters and in situ stress on hydraulic fracture patterns are then quantitatively assessed.The study reveals that shear-induced deformation primarily governs the fracturing morphology in the GR1 well,driven by smaller injection rates and viscosities that promote massive activation of natural fractures,ultimately dominating the direction of hydraulic fracturing.Furthermore,the increase of in situ differential stress may promote shear damage of natural fracture surfaces,with the exact influence pattern depending on the combination of specific discontinuity properties and in situ stress state.Finally,we provide recommendations for EGS fracturing based on the influence characteristics of multiple parameters.This study can serve as an effective basis and reference for the design and optimization of EGS in the Gonghe basin and other sites.展开更多
In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symm...In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symmetric points,numerical simulations are conducted to one-soliton and two-soliton solutions of a discrete KdV equation,as well as a one-soliton solution of a discrete Toda lattice equation.Compared with the existing discrete deep learning approach,the numerical results reveal that within the specified spatiotemporal domain,the prediction accuracy by SDE-PINN is excellent regardless of the interior or extrapolation prediction,with a significant reduction in training time.The proposed data enhancement technique and symmetric structure development provides a new perspective for the deep learning approach to solve discrete NLEs.The newly proposed SDE-PINN can also be applied to solve continuous nonlinear equations and other discrete NLEs numerically.展开更多
The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of ...The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of an open pit slope.For this purpose,spatially conditioned DFN models were developed for the pit walls at Tasiast mine using comprehensive structural data from the mine.Using Sequential Gaussian Simulation(SGS),volumetric fracture intensities(P32)were modeled across the entire mine site in the form of 3D block models.The simulated P32 block models were used as the input constraints for conditional DFN fracture generation,where the DFN grid dimension is the same as the SGS 3D blocks.The spatially constrained DFN models were further calibrated using aerial fracture intensities(P21)data from the pit walls,obtained by a survey of the pit walls using an unmanned aerial vehicle(UAV)and measured traces of joints from 3D point cloud data.The final DFN model is expected to honor the fracture intensities gathered through different means with optimal model accuracy.Finally,bench-scale and interramp scale rock wedge slope stability analyses were conducted using the calibrated conditional DFN models.This work proves the significance of conditioned DFN models in rock wedge stability analysis.Such models provide detailed information regarding rock wedge stability so that site monitoring and prevention plans can be conducted with higher efficiency.展开更多
The manner and conditions of running the decision-making system with self-defense electronic jamming are given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making with se...The manner and conditions of running the decision-making system with self-defense electronic jamming are given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making with self-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discrete dynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discrete dynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesize different targets which are not predominant. In this way, various features at the same time, as well as the same feature appearing at different time complement mutually; in addition, the accuracy and reliability of electronic jamming decision making are enhanced significantly.展开更多
Terrestrial laser scanning(TLS) is a useful technology for rock mass characterization. A laser scanner produces a massive point cloud of a scanned area, such as an exposed rock surface in an underground tunnel,with mi...Terrestrial laser scanning(TLS) is a useful technology for rock mass characterization. A laser scanner produces a massive point cloud of a scanned area, such as an exposed rock surface in an underground tunnel,with millimeter precision. The density of the point cloud depends on several parameters from both the TLS operational conditions and the specifications of the project, such as the resolution and the quality of the laser scan, the section of the tunnel, the distance between scanning stations, and the purpose of the scans. One purpose of the scan can be to characterize the rock mass and statistically analyze the discontinuities that compose it for further discontinuous modeling. In these instances, additional data processing and a detailed analysis should be performed on the point cloud to extract the parameters to define a discrete fracture network(DFN) for each discontinuity set. I-site studio is a point cloud processing software that allows users to edit and process laser scans. This software contains a set of geotechnical analysis tools that assist engineers during the structural mapping process, allowing for greater and more representative data regarding the structural information of the rock mass, which may be used for generating DFNs. This paper presents the procedures used during a laser scan for characterizing discontinuities in an underground limestone mine and the results of the scan as applied to the generation of DFNs for further discontinuous modeling.展开更多
Heterogeneity is an inherent component of rock and may be present in different forms including mineralheterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks areusually observed ...Heterogeneity is an inherent component of rock and may be present in different forms including mineralheterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks areusually observed in crystalline rocks in two forms： natural and stress-induced; the amount of stressinducedmicrocracking increases with depth and in-situ stress. Laboratory results indicate that thephysical properties of rocks such as strength, deformability, P-wave velocity and permeability areinfluenced by increase in microcrack intensity. In this study, the finite-discrete element method （FDEM）is used to model microcrack heterogeneity by introducing into a model sample sets of microcracks usingthe proposed micro discrete fracture network （mDFN） approach. The characteristics of the microcracksrequired to create mDFN models are obtained through image analyses of thin sections of Lac du Bonnetgranite adopted from published literature. A suite of two-dimensional laboratory tests including uniaxial,triaxial compression and Brazilian tests is simulated and the results are compared with laboratory data.The FDEM-mDFN models indicate that micro-heterogeneity has a profound influence on both the mechanicalbehavior and resultant fracture pattern. An increase in the microcrack intensity leads to areduction in the strength of the sample and changes the character of the rock strength envelope. Spallingand axial splitting dominate the failure mode at low confinement while shear failure is the dominantfailure mode at high confinement. Numerical results from simulated compression tests show thatmicrocracking reduces the cohesive component of strength alone, and the frictional strength componentremains unaffected. Results from simulated Brazilian tests show that the tensile strength is influenced bythe presence of microcracks, with a reduction in tensile strength as microcrack intensity increases. Theimportance of microcrack heterogeneity in reproducing a bi-linear or S-shape failure envelope and itseffects on the mechanisms leading to spalling damage near an underground opening are also discussed.展开更多
Fractured reservoirs are an important target for oil and gas exploration in the Tarim Basin and the prediction of this type of reservoir is challenging.Due to the complicated fracture system in the Tarim Basin,the con...Fractured reservoirs are an important target for oil and gas exploration in the Tarim Basin and the prediction of this type of reservoir is challenging.Due to the complicated fracture system in the Tarim Basin,the conventional AVO inversion method based on HTI theory to predict fracture development will result in some errors.Thus,an integrated research concept for fractured reservoir prediction is put forward in this paper.Seismic modeling plays a bridging role in this concept,and the establishment of an anisotropic fracture model by Discrete Fracture Network （DFN） is the key part.Because the fracture system in the Tarim Basin shows complex anisotropic characteristics,it is vital to build an effective anisotropic model.Based on geological,well logging and seismic data,an effective anisotropic model of complex fracture systems can be set up with the DFN method.The effective elastic coefficients,and the input data for seismic modeling can be calculated.Then seismic modeling based on this model is performed,and the seismic response characteristics are analyzed.The modeling results can be used in the following AVO inversion for fracture detection.展开更多
Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important con...Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.展开更多
This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point,...This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov-Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result.展开更多
The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target thr...The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.展开更多
A discrete predictor-based control method is developed for a class of linear time-invariant networked control systems with a sensor-to-controller time-varying delay and a controller-to-actuator uncertain constant dela...A discrete predictor-based control method is developed for a class of linear time-invariant networked control systems with a sensor-to-controller time-varying delay and a controller-to-actuator uncertain constant delay,which can be potentially applied to vision-based control systems.The control scheme is composed of a state prediction and a discrete predictor-based controller.The state prediction is used to compensate for the effect of the sensor-to-controller delay,and the system can be stabilized by the discrete predictor-based controller.Moreover,it is shown that the control scheme is also robust with respect to slight message rejections.Finally,the main theoretical results are illustrated by simulation results and experimental results based on a networked visual servo inverted pendulum system.展开更多
Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This st...Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.展开更多
Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems ofte...Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.展开更多
Discrete Hopfield neural network with delay is an extension of discrete Hopfield neural network.As it is well known,the stability of neural networks is not only the most basic and important problem but also foundation...Discrete Hopfield neural network with delay is an extension of discrete Hopfield neural network.As it is well known,the stability of neural networks is not only the most basic and important problem but also foundation of the network's applications.The stability of discrete HJopfield neural networks with delay is mainly investigated by using Lyapunov function.The sufficient conditions for the networks with delay converging towards a limit cycle of length 4 are obtained.Also,some sufficient criteria are given to ensure the networks having neither a stable state nor a limit cycle with length 2.The obtained results here generalize the previous results on stability of discrete Hopfield neural network with delay and without delay.展开更多
To efficiently predict the mechanical parameters of granular soil based on its random micro-structure,this study proposed a novel approach combining numerical simulation and machine learning algorithms.Initially,3500 ...To efficiently predict the mechanical parameters of granular soil based on its random micro-structure,this study proposed a novel approach combining numerical simulation and machine learning algorithms.Initially,3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional(3D)discrete element method(DEM)were conducted to construct a database.In this process,the positions of the particles were randomly altered,and the particle assemblages changed.Interestingly,besides confirming the influence of particle size distribution parameters,the stress-strain curves differed despite an identical gradation size statistic when the particle position varied.Subsequently,the obtained data were partitioned into training,validation,and testing datasets at a 7:2:1 ratio.To convert the DEM model into a multi-dimensional matrix that computers can recognize,the 3D DEM models were first sliced to extract multi-layer two-dimensional(2D)cross-sectional data.Redundant information was then eliminated via gray processing,and the data were stacked to form a new 3D matrix representing the granular soil’s fabric.Subsequently,utilizing the Python language and Pytorch framework,a 3D convolutional neural networks(CNNs)model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil’s fabric.The mean squared error(MSE)function was utilized to assess the loss value during the training process.When the learning rate(LR)fell within the range of 10-5e10-1,and the batch sizes(BSs)were 4,8,16,32,and 64,the loss value stabilized after 100 training epochs in the training and validation dataset.For BS?32 and LR?10-3,the loss reached a minimum.In the testing set,a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error(MAPE)of 4.43%under the optimized condition,demonstrating the accuracy of this approach.Thus,by combining DEM and CNNs,the variation of soil’s mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.展开更多
In this paper, by using both the linear stability analysis and Lyapunov function approach, some conditions for stabilizing synchronization behavior in a discrete-time complex dynamical network were derived. These cond...In this paper, by using both the linear stability analysis and Lyapunov function approach, some conditions for stabilizing synchronization behavior in a discrete-time complex dynamical network were derived. These conditions were determined by the coupling strength and the eigenvalues of coupling configuration matrix. Furthermore, some explicit results were obtained when the coupling map between the nodes is equal to the dynamics function of the network, which implies that the product of the coupling strength and the eigenvalues is bounded.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52574103 and 42277150).
文摘Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology remain largely unexplored.Leveraging the advantages of the finite-discrete element method(FDEM)for explicitly simulating fracture propagation and the strengths of the unifiedpipe model(UPM)for efficientlymodeling dual-permeability seepage,we propose a new hydromechanical(HM)coupling approach for modeling hydraulic fracturing.Validated against benchmark examples,the proposed FDEM-UPM model is further augmented by incorporating a Fourier-based methodology for reconstructing non-planar fractures,enabling quantitative analysis of hydraulic fracturing behavior within rough discrete fracture networks(DFNs).The FDEM-UPM model demonstrates computational advantages in accurately capturing transient hydraulic seepage phenomena,while the asynchronous time-stepping schemes between hydraulic and mechanical analyses substantially enhanced computational efficiencywithout compromising computational accuracy.Our results show that fracture morphology can affect both macroscopic fracture networks and microscopic interaction types between hydraulic fractures(HFs)and natural fractures(NFs).In an isotropic stress field,the initiation azimuth,propagation direction and microcracking mechanism are significantly influencedby fracture roughness.In an anisotropic stress field,HFs invariably propagate parallel to the direction of the maximum principal stress,reducing the overall complexity of the stimulated fracture networks.Additionally,stress concentration and perturbation attributed to fracture morphology tend to be compromised as the leak-off increases,while the breakdown and propagation pressures remain insensitive to fracture morphology.These findingsprovide new insights into the hydraulic fracturing mechanisms of fractured reservoirs containing complex rough DFNs.
基金Australian Research Council Linkage Program(LP200301404)for sponsoring this researchthe financial support provided by the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology,SKLGP2021K002)National Natural Science Foundation of China(52374101,32111530138).
文摘Discrete fracture network(DFN)commonly existing in natural rock masses plays an important role in geological complexity which can influence rock fracturing behaviour during fluid injection.This paper simulated the hydraulic fracturing process in lab-scale coal samples with DFNs and the induced seismic activities by the discrete element method(DEM).The effects of DFNs on hydraulic fracturing,induced seismicity and elastic property changes have been concluded.Denser DFNs can comprehensively decrease the peak injection pressure and injection duration.The proportion of strong seismic events increases first and then decreases with increasing DFN density.In addition,the relative modulus of the rock mass is derived innovatively from breakdown pressure,breakdown fracture length and the related initiation time.Increasing DFN densities among large(35–60 degrees)and small(0–30 degrees)fracture dip angles show opposite evolution trends in relative modulus.The transitional point(dip angle)for the opposite trends is also proportionally affected by the friction angle of the rock mass.The modelling results have much practical meaning to infer the density and geometry of pre-existing fractures and the elastic property of rock mass in the field,simply based on the hydraulic fracturing and induced seismicity monitoring data.
基金supported by National Natural Science Foundation of China(Grant Nos.52074312 and 52211530097)CNPC Science and Technology Innovation Foundation(Grant No.2021DQ02-0505).
文摘Wellbore breakout is one of the critical issues in drilling due to the fact that the related problems result in additional costs and impact the drilling scheme severely.However,the majority of such wellbore breakout analyses were based on continuum mechanics.In addition to failure in intact rocks,wellbore breakouts can also be initiated along natural discontinuities,e.g.weak planes and fractures.Furthermore,the conventional models in wellbore breakouts with uniform distribution fractures could not reflect the real drilling situation.This paper presents a fully coupled hydro-mechanical model of the SB-X well in the Tarim Basin,China for evaluating wellbore breakouts in heavily fractured rocks under anisotropic stress states using the distinct element method(DEM)and the discrete fracture network(DFN).The developed model was validated against caliper log measurement,and its stability study was carried out by stress and displacement analyses.A parametric study was performed to investigate the effects of the characteristics of fracture distribution(orientation and length)on borehole stability by sensitivity studies.Simulation results demonstrate that the increase of the standard deviation of orientation when the fracture direction aligns parallel or perpendicular to the principal stress direction aggravates borehole instability.Moreover,an elevation in the average fracture length causes the borehole failure to change from the direction of the minimum in-situ horizontal principal stress(i.e.the direction of wellbore breakouts)towards alternative directions,ultimately leading to the whole wellbore failure.These findings provide theoretical insights for predicting wellbore breakouts in heavily fractured rocks.
基金supported by the Natural Science Foundation of Hunan Province(Grant No.2025JJ50368)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.24A0248)the Guiding Science and Technology Plan Project of Changsha City(Grant No.kzd2501129)。
文摘In recent years,discrete neuron and discrete neural network models have played an important role in the development of neural dynamics.This paper reviews the theoretical advantages of well-known discrete neuron models,some existing discretized continuous neuron models,and discrete neural networks in simulating complex neural dynamics.It places particular emphasis on the importance of memristors in the composition of neural networks,especially their unique memory and nonlinear characteristics.The integration of memristors into discrete neural networks,including Hopfield networks and their fractional-order variants,cellular neural networks and discrete neuron models has enabled the study and construction of various neural models with memory.These models exhibit complex dynamic behaviors,including superchaotic attractors,hidden attractors,multistability,and synchronization transitions.Furthermore,the present paper undertakes an analysis of more complex dynamical properties,including synchronization,speckle patterns,and chimera states in discrete coupled neural networks.This research provides new theoretical foundations and potential applications in the fields of brain-inspired computing,artificial intelligence,image encryption,and biological modeling.
基金support from the National Natural Science Foundation of China(Grant Nos.42320104003,42177175,and 42077247)the Fundamental Research Funds for the Central Universities.
文摘Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overall direction of hydraulic fractures does not show a delineated shape parallel to the maximum principal stress orientation.A field-scale numerical model based on the distinct element method is set up to carry out a fully coupled hydromechanical simulation,with the explicit representation of natural fractures via the discrete fracture network(DFN)approach.The effects of injection parameters and in situ stress on hydraulic fracture patterns are then quantitatively assessed.The study reveals that shear-induced deformation primarily governs the fracturing morphology in the GR1 well,driven by smaller injection rates and viscosities that promote massive activation of natural fractures,ultimately dominating the direction of hydraulic fracturing.Furthermore,the increase of in situ differential stress may promote shear damage of natural fracture surfaces,with the exact influence pattern depending on the combination of specific discontinuity properties and in situ stress state.Finally,we provide recommendations for EGS fracturing based on the influence characteristics of multiple parameters.This study can serve as an effective basis and reference for the design and optimization of EGS in the Gonghe basin and other sites.
基金supported by the National Natural Science Foundation of China(Grant No.12071042)the Beijing Natural Science Foundation(Grant No.1202004)Promoting the Development of University Classification-Student Innovation and Entrepreneurship Training Programme(Grant No.5112410857)。
文摘In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symmetric points,numerical simulations are conducted to one-soliton and two-soliton solutions of a discrete KdV equation,as well as a one-soliton solution of a discrete Toda lattice equation.Compared with the existing discrete deep learning approach,the numerical results reveal that within the specified spatiotemporal domain,the prediction accuracy by SDE-PINN is excellent regardless of the interior or extrapolation prediction,with a significant reduction in training time.The proposed data enhancement technique and symmetric structure development provides a new perspective for the deep learning approach to solve discrete NLEs.The newly proposed SDE-PINN can also be applied to solve continuous nonlinear equations and other discrete NLEs numerically.
基金Kinross Gold and MITACS for their financial support(Grant No.FR42880).
文摘The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of an open pit slope.For this purpose,spatially conditioned DFN models were developed for the pit walls at Tasiast mine using comprehensive structural data from the mine.Using Sequential Gaussian Simulation(SGS),volumetric fracture intensities(P32)were modeled across the entire mine site in the form of 3D block models.The simulated P32 block models were used as the input constraints for conditional DFN fracture generation,where the DFN grid dimension is the same as the SGS 3D blocks.The spatially constrained DFN models were further calibrated using aerial fracture intensities(P21)data from the pit walls,obtained by a survey of the pit walls using an unmanned aerial vehicle(UAV)and measured traces of joints from 3D point cloud data.The final DFN model is expected to honor the fracture intensities gathered through different means with optimal model accuracy.Finally,bench-scale and interramp scale rock wedge slope stability analyses were conducted using the calibrated conditional DFN models.This work proves the significance of conditioned DFN models in rock wedge stability analysis.Such models provide detailed information regarding rock wedge stability so that site monitoring and prevention plans can be conducted with higher efficiency.
基金the National Natural Science Fundation of China (10377014).
文摘The manner and conditions of running the decision-making system with self-defense electronic jamming are given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making with self-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discrete dynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discrete dynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesize different targets which are not predominant. In this way, various features at the same time, as well as the same feature appearing at different time complement mutually; in addition, the accuracy and reliability of electronic jamming decision making are enhanced significantly.
基金funded by the NIOSH Mining Program under Contract No. 200-2016-91300
文摘Terrestrial laser scanning(TLS) is a useful technology for rock mass characterization. A laser scanner produces a massive point cloud of a scanned area, such as an exposed rock surface in an underground tunnel,with millimeter precision. The density of the point cloud depends on several parameters from both the TLS operational conditions and the specifications of the project, such as the resolution and the quality of the laser scan, the section of the tunnel, the distance between scanning stations, and the purpose of the scans. One purpose of the scan can be to characterize the rock mass and statistically analyze the discontinuities that compose it for further discontinuous modeling. In these instances, additional data processing and a detailed analysis should be performed on the point cloud to extract the parameters to define a discrete fracture network(DFN) for each discontinuity set. I-site studio is a point cloud processing software that allows users to edit and process laser scans. This software contains a set of geotechnical analysis tools that assist engineers during the structural mapping process, allowing for greater and more representative data regarding the structural information of the rock mass, which may be used for generating DFNs. This paper presents the procedures used during a laser scan for characterizing discontinuities in an underground limestone mine and the results of the scan as applied to the generation of DFNs for further discontinuous modeling.
文摘Heterogeneity is an inherent component of rock and may be present in different forms including mineralheterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks areusually observed in crystalline rocks in two forms： natural and stress-induced; the amount of stressinducedmicrocracking increases with depth and in-situ stress. Laboratory results indicate that thephysical properties of rocks such as strength, deformability, P-wave velocity and permeability areinfluenced by increase in microcrack intensity. In this study, the finite-discrete element method （FDEM）is used to model microcrack heterogeneity by introducing into a model sample sets of microcracks usingthe proposed micro discrete fracture network （mDFN） approach. The characteristics of the microcracksrequired to create mDFN models are obtained through image analyses of thin sections of Lac du Bonnetgranite adopted from published literature. A suite of two-dimensional laboratory tests including uniaxial,triaxial compression and Brazilian tests is simulated and the results are compared with laboratory data.The FDEM-mDFN models indicate that micro-heterogeneity has a profound influence on both the mechanicalbehavior and resultant fracture pattern. An increase in the microcrack intensity leads to areduction in the strength of the sample and changes the character of the rock strength envelope. Spallingand axial splitting dominate the failure mode at low confinement while shear failure is the dominantfailure mode at high confinement. Numerical results from simulated compression tests show thatmicrocracking reduces the cohesive component of strength alone, and the frictional strength componentremains unaffected. Results from simulated Brazilian tests show that the tensile strength is influenced bythe presence of microcracks, with a reduction in tensile strength as microcrack intensity increases. Theimportance of microcrack heterogeneity in reproducing a bi-linear or S-shape failure envelope and itseffects on the mechanisms leading to spalling damage near an underground opening are also discussed.
基金co-supported by the National Basic Research Program of China(Grant No.2011CB201103)the National Science and Technology Major Project(GrantNo.2011ZX05004003)
文摘Fractured reservoirs are an important target for oil and gas exploration in the Tarim Basin and the prediction of this type of reservoir is challenging.Due to the complicated fracture system in the Tarim Basin,the conventional AVO inversion method based on HTI theory to predict fracture development will result in some errors.Thus,an integrated research concept for fractured reservoir prediction is put forward in this paper.Seismic modeling plays a bridging role in this concept,and the establishment of an anisotropic fracture model by Discrete Fracture Network （DFN） is the key part.Because the fracture system in the Tarim Basin shows complex anisotropic characteristics,it is vital to build an effective anisotropic model.Based on geological,well logging and seismic data,an effective anisotropic model of complex fracture systems can be set up with the DFN method.The effective elastic coefficients,and the input data for seismic modeling can be calculated.Then seismic modeling based on this model is performed,and the seismic response characteristics are analyzed.The modeling results can be used in the following AVO inversion for fracture detection.
基金This work was financially supported by the National Key R&D Program of China(No.2021YFC2900500)the National Natural Science Foundation of China(Nos.52074020 and 42202306)+2 种基金the Open Fund of State Key Laboratory of Water Resource Protection and Utilization in Coal Mining(No.WPUKFJJ2019-06)the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(No.FRF-IDRY-21001)the Natural Science Foundation of Jiangsu Province,China(No.BK20200993).
文摘Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.
基金Project supported by the National Natural Science Foundations of China(Grant No.70871056)the Society Science Foundation from Ministry of Education of China(Grant No.08JA790057)the Advanced Talents'Foundation and Student's Foundation of Jiangsu University,China(Grant Nos.07JDG054 and 07A075)
文摘This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov-Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result.
基金supported by the Fundamental Scientific Research Business Expenses for Central Universities(3072021CFJ0803)the Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Key Laboratory Project(AMCIT21V3).
文摘The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.
基金supported by the China Scholarship Council(CSC)the National Natural Science Foundation of China(92067106)。
文摘A discrete predictor-based control method is developed for a class of linear time-invariant networked control systems with a sensor-to-controller time-varying delay and a controller-to-actuator uncertain constant delay,which can be potentially applied to vision-based control systems.The control scheme is composed of a state prediction and a discrete predictor-based controller.The state prediction is used to compensate for the effect of the sensor-to-controller delay,and the system can be stabilized by the discrete predictor-based controller.Moreover,it is shown that the control scheme is also robust with respect to slight message rejections.Finally,the main theoretical results are illustrated by simulation results and experimental results based on a networked visual servo inverted pendulum system.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3080200)the National Natural Science Foundation of China(Grant No.42022053)the China Postdoctoral Science Foundation(Grant No.2023M731264).
文摘Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of ChinaProjects(012BAF10B11,2012BAF10B06)supported by the National Key Technologies R&D Program of China+1 种基金Project(F11-264-1-08)supported by the Shenyang Science and Technology Project,ChinaProject(2011BY100383)supported by the Cooperation Project of Foshan and Chinese Academy of Sciences
文摘Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.
基金supported by China Postdoctoral Science Foundation(2003033516)partly supported by Open Founda-tion of University Key Lab of Information Sciences and Engineering,Dalian University,
文摘Discrete Hopfield neural network with delay is an extension of discrete Hopfield neural network.As it is well known,the stability of neural networks is not only the most basic and important problem but also foundation of the network's applications.The stability of discrete HJopfield neural networks with delay is mainly investigated by using Lyapunov function.The sufficient conditions for the networks with delay converging towards a limit cycle of length 4 are obtained.Also,some sufficient criteria are given to ensure the networks having neither a stable state nor a limit cycle with length 2.The obtained results here generalize the previous results on stability of discrete Hopfield neural network with delay and without delay.
基金supported by the National Key R&D Program of China (Grant No.2022YFC3003401)the National Natural Science Foundation of China (Grant Nos.42041006 and 42377137).
文摘To efficiently predict the mechanical parameters of granular soil based on its random micro-structure,this study proposed a novel approach combining numerical simulation and machine learning algorithms.Initially,3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional(3D)discrete element method(DEM)were conducted to construct a database.In this process,the positions of the particles were randomly altered,and the particle assemblages changed.Interestingly,besides confirming the influence of particle size distribution parameters,the stress-strain curves differed despite an identical gradation size statistic when the particle position varied.Subsequently,the obtained data were partitioned into training,validation,and testing datasets at a 7:2:1 ratio.To convert the DEM model into a multi-dimensional matrix that computers can recognize,the 3D DEM models were first sliced to extract multi-layer two-dimensional(2D)cross-sectional data.Redundant information was then eliminated via gray processing,and the data were stacked to form a new 3D matrix representing the granular soil’s fabric.Subsequently,utilizing the Python language and Pytorch framework,a 3D convolutional neural networks(CNNs)model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil’s fabric.The mean squared error(MSE)function was utilized to assess the loss value during the training process.When the learning rate(LR)fell within the range of 10-5e10-1,and the batch sizes(BSs)were 4,8,16,32,and 64,the loss value stabilized after 100 training epochs in the training and validation dataset.For BS?32 and LR?10-3,the loss reached a minimum.In the testing set,a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error(MAPE)of 4.43%under the optimized condition,demonstrating the accuracy of this approach.Thus,by combining DEM and CNNs,the variation of soil’s mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.
基金Project supported by the National Natural Science Foundation of China （Grant No.10471087）, and Science Foundation of Shanghai Municipal Commission of Education （Grant No.03AK33）
文摘In this paper, by using both the linear stability analysis and Lyapunov function approach, some conditions for stabilizing synchronization behavior in a discrete-time complex dynamical network were derived. These conditions were determined by the coupling strength and the eigenvalues of coupling configuration matrix. Furthermore, some explicit results were obtained when the coupling map between the nodes is equal to the dynamics function of the network, which implies that the product of the coupling strength and the eigenvalues is bounded.
