The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismi...The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismic wave simulation with a data-constrained finite-fault rupture model.The constraint is implemented by identifying the optimal ground motion models(GMMs)through a scoring system that selects the best-fit GMMs to mid-and far-field China Earthquake Networks Center(CENC)seismic network data;and applying the optimal GMMs to refine the rupture model parameters for near-fault intensity field simulation.The simulated near-fault seismic intensity field reproduces seismic intensities collected from Myanmar’s sparse seismic network and concentrated in≥Ⅷintensity zones within 50 km of the projected fault plane;and identifies abnormal intensity regions exhibiting≥Ⅹintensity along the Meiktila-Naypyidaw corridor and near Shwebo that are attributed to soft soil amplification effects and near-fault directivity.This framework can also be applied to post-earthquake assessments in other similar regions.展开更多
This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,...This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,particularly suited for regions with limited seismic data.Herein,we report a generative adversarial network(GAN)framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records.The constructed GAN model generates ground motions based on continuous physical variables such as source distance,site conditions,and magnitude,effectively capturing the complexity and diversity of ground motions under different scenarios.This capability allows the proposed model to approximate real seismic data,making it applicable to a wide range of engineering purposes.Using the Shandong Pingyuan earthquake as an example,a specialized dataset was constructed based on regional real ground motion records.The response spectrum at target locations was obtained through inverse distance-weighted interpolation of actual response spectra,followed by continuous wavelet transform to derive the ground motion time histories at these locations.Through iterative parameter adjustments,the constructed GAN model learned the probability distribution of strong-motion data for this event.The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics,accurately reflecting the non-stationary nature of seismic records.Statistical comparisons between synthetic and real response spectra,waveform envelopes,and peak ground acceleration show a high degree of similarity,underscoring the effectiveness of the model in replicating both the statistical and physical characteristics of real ground motions.These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions,providing a reliable approach for enriching regional ground motion databases.Additionally,the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.展开更多
In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled ...In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.展开更多
The diversity and complexity of the user population on the campus network increase the risk of computer virus infection during terminal information interactions.Therefore,it is crucial to explore how computer viruses ...The diversity and complexity of the user population on the campus network increase the risk of computer virus infection during terminal information interactions.Therefore,it is crucial to explore how computer viruses propagate between terminals in such a network.In this study,we establish a novel computer virus spreading model based on the characteristics of the basic network structure and a classical epidemic-spreading dynamics model,adapted to real-world university scenarios.The proposed model contains six groups:susceptible,unisolated latent,isolated latent,infection,recovery,and crash.We analyze the proposed model's basic reproduction number and disease-free equilibrium point.Using real-world university terminal computer virus propagation data,a basic computer virus infection rate,a basic computer virus removal rate,and a security protection strategy deployment rate are proposed to define the conversion probability of each group and perceive each group's variation tendency.Furthermore,we analyze the spreading trend of computer viruses in the campus network in terms of the proposed computer virus spreading model.We propose specific measures to suppress the spread of computer viruses in terminals,ensuring the safe and stable operation of the campus network terminals to the greatest extent.展开更多
The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface ...The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.展开更多
Seismic quantitative reservoir simulations and characterizations have played a vital role in exploring stratigraphic traps,such as lateaggradational lowstands prograding wedge systems(LPWS)within lowstands systems tra...Seismic quantitative reservoir simulations and characterizations have played a vital role in exploring stratigraphic traps,such as lateaggradational lowstands prograding wedge systems(LPWS)within lowstands systems tracts(LST).However,seismic data acquisition operations are always dominated by exceptional seismic coherent noise events,e.g.,multiples,which reduce the signal strengths of the sourcegenerated incident seismic waves within vertically and laterally heterogeneous earth systems.Hence,these noise events create hurdles in predicting paleo-depositional impedance(PDI),paleo-thickness(PTS),paleo-dense fractured networks,erosional and depositional zones,faultcontrolled migrations,and types of seismic reflection configurations(SRFC),which are key elements in developing stratigraphic pinch-out traps.This research utilizes the state-of-the-art technologies of spectral wavelet-based instantaneous time-frequency analysis and seismic waveform frequency-controlled porosity-constrained static reservoir simulation(FDPVS)tools to quantify the LPWS inside the Onshore Basin,Pakistan.The use of conventional amplitude-based seismic attributes,such as the average energy,remained a better tool for deciphering the overall geological architecture of the LPWS.Conventional FDPVS realizations resolved a PDI of−1.391 gm./c.c.^(*)m/s to−0.97 gm./c.c.^(*)m/s for LPWS with PTS of 12 and 20 m,respectively.A 0.9 km lateral extent of paleo-dense fractured networks(PDFN)with a strong linear regression R^(2)=0.93 was also resolved.Average energy attribute-based instantaneous frequency FDPVS realizations enabled the imaging of parallel-toprograding SRFC with resolved magnitudes of−0.259 gm./c.c.^(*)m/s for PDI,20 m for PTS,and 0.73 km for PDFN with linear regression transforms at R^(2)=0.92,which indicates the deposition of onlap fill facies inside the LPWS during extensive sea-level fall.These realizations have also resolved frequency-controlled fault migrations on 27-Hz spectral waveform-based amplitude plots with 2.174 gm./c.c.^(*)m/s PDI for conduit fault systems and 27-Hz with 0.585 gm./c.c.^(*)m/s PDI for sealing fault systems.All these structural configurations are completely sealed up by transgressive seals of transgressive systems tracts and,hence,developed into pure stratigraphic-based oil and gas plays.This research has strong implications for side-tracking drilling locations and provides an analogue for basins with similar geology and stratigraphy worldwide.展开更多
Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCa...Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCards,Online Mendelian Inheritance in Man,Gene Expression Omnibus,and Comparative Toxicogenomics Database,the intersection core targets of CUR and diabetic retinopathy were identified.The intersection target was imported into the STRING database to obtain the protein-protein interaction map.According to the Database for Annotation,Visualization and Integrated Discovery database,the intersected targets were enriched in Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways.Then Cytoscape 3.9.1 is used to make the drug-target-disease-pathway network.The mechanism of CUR and diabetic retinopathy was further verified by molecular docking and molecular dynamics simulation.Results:There were 203 intersecting targets of CUR and diabetic retinopathy identified.1320 GO entries were enriched for GO functions,which were primarily involved in the composition of cells such as identical protein binding,protein binding,enzyme binding,etc.It was found that 175 pathways were enriched using Kyoto Encyclopedia of Genes and Genomes pathway enrichment methods,which were mainly included in the lipid and atherosclerosis,AGE-RAGE signaling pathway in diabetic complications,pathways in cancer,etc.In the molecular docking analysis,CUR was found to have a good ability to bind to the core targets of albumin,IL-1B,and IL-6.The binding of albumin to CUR was further verified by molecular dynamics simulation.Conclusion:As a result of this study,CUR may exert a role in the treatment of diabetic retinopathy through multi-target and multi-pathway regulation,which indicates a possible direction of future research.展开更多
The simulation techniques of hardware-in-loop simulation(HLS) of homing antitank missile based on the personal computer (PC) are discussed. The PC and MCS-96 chip controller employ A/D and D/A boards (with photoelectr...The simulation techniques of hardware-in-loop simulation(HLS) of homing antitank missile based on the personal computer (PC) are discussed. The PC and MCS-96 chip controller employ A/D and D/A boards (with photoelectricity isolation) to transfer measur ment and control information about homing head, gyro and rudder and utilize the digital hand shaking board to build correct communication communication protocol. In order to satisfy the real-time requirement of HLS, this paper first simplifies the aerodynamic data file reasonably, then builds a PC software with C language. The program of the controller part is made with PL/M language. The simulation of HLS based on PC is done with the same sampling period of 10ms as that of YH-F1 and the experiment results are identical to those of digital simulation of the homing anti-tank guided missile.展开更多
Cadmium selenide(CdSe)is an inorganic semiconductor with unique optical and electronic properties that make it useful in various applications,including solar cells,light-emitting diodes,and biofluorescent tagging.In o...Cadmium selenide(CdSe)is an inorganic semiconductor with unique optical and electronic properties that make it useful in various applications,including solar cells,light-emitting diodes,and biofluorescent tagging.In order to synthesize high-quality crystals and subsequently integrate them into devices,it is crucial to understand the atomic scale crystallization mechanism of CdSe.Unfortunately,such studies are still absent in the literature.To overcome this limitation,we employed an enhanced sampling-accelerated active learning approach to construct a deep neural potential with ab initio accuracy for studying the crystallization of CdSe.Our brute-force molecular dynamics simulations revealed that a spherical-like nu-cleus formed spontaneously and stochastically,resulting in a stacking disordered structure where the competition between hexagonal wurtzite and cubic zinc blende polymorphs is temperature-dependent.We found that pure hexagonal crystal can only be obtained approximately above 1430 K,which is 35 K below its melting temperature.Furthermore,we observed that the solidification dynamics of Cd and Se atoms were distinct due to their different diffusion coefficients.The solidification process was initiated by lower mobile Se atoms forming tetrahedral frameworks,followed by Cd atoms occupying these tetra-hedral centers and settling down until the third-shell neighbor of Se atoms sited on their lattice posi-tions.Therefore,the medium-range ordering of Se atoms governs the crystallization process of CdSe.Our findings indicate that understanding the complex dynamical process is the key to comprehending the crystallization mechanism of compounds like CdSe,and can shed lights in the synthesis of high-quality crystals.展开更多
The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied b...The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied by molecular dynamics （MD） simulation. It was found that a closed three-dimensional misfit dislocation network appears on the γ/γ＇ phase interfaces, and the shape of the dislocation network is independent of the lattice mismatch. Under the influence of the temperature, the dislocation network gradually becomes irregular, a/2 [110] dislocations in the γ matrix phase emit and partly cut into the γ＇ phase with the increase in temperature. The dislocation evolution is related to the local stress field, a peak stress occurs at γ/γ＇ phase interface, and with the increase in temperature and relaxation times, the stress in the γ phase gradually increases, the number of dislocations in the γ phase increases and cuts into γ＇ phase from the interfaces where dislocation network is damaged. The results provide important information for understanding the temperature dependence of the dislocation evolution and mechanical properties of Ni-based single-crystal superalloys.展开更多
The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural ...The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).展开更多
In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I &a...In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.展开更多
A simulation model was proposed to investigate the relationship between train delays and passenger delays and to predict the dynamic passenger distribution in a large-scale rail transit network. It was assumed that th...A simulation model was proposed to investigate the relationship between train delays and passenger delays and to predict the dynamic passenger distribution in a large-scale rail transit network. It was assumed that the time varying original-destination demand and passenger path choice probability were given. Passengers were assumed not to change their destinations and travel paths after delay occurs. CapaciW constraints of train and queue rules of alighting and boarding were taken into account. By using the time-driven simulation, the states of passengers, trains and other facilities in the network were updated every time step. The proposed methodology was also tested in a real network, for demonstration. The results reveal that short train delay does not necessarily result in passenger delays, while, on the contrary, some passengers may get benefits from the short delay. However, large initial train delay may result in not only knock-on train and passenger delays along the same line, but also the passenger delays across the entire rail transit network.展开更多
This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipelin...This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipeline systems (CFD-simulator). The approach used in CFD-simulators for modeling gas mixture transmission through long, branched, multi-section pipelines is based on tailoring the full system of fluid dynamics equations to conditions of unsteady, non-isothermal processes of the gas mixture flow. Identification, in a CFD-simulator, of safe parameters for gas transmission through compressor stations amounts to finding the interior points of admissible sets described by systems of nonlinear algebraic equalities and inequalities. Such systems of equalities and inequalities comprise a formal statement of technological, design, operational and other constraints to which operation of the network equipment is subject. To illustrate the practicability of the method of numerical simulation of a gas transmission network, we compare computation results and gas flow parameters measured on-site at the gas transmission enter-prise.展开更多
Stress sensitivity is a very important index to understand the seepage characteristics of a reservoir.In this study,dedicated experiments and theoretical arguments based on the visualization of porous media are used t...Stress sensitivity is a very important index to understand the seepage characteristics of a reservoir.In this study,dedicated experiments and theoretical arguments based on the visualization of porous media are used to assess the effects of the fracture angle,spacing,and relevant elastic parameters on the principal value of the permeability tensor.The fracture apertures at different angles show different change rates,which influence the relative permeability for different sets of fractures.Furthermore,under the same pressure condition,the fractures with different angles show different degrees of deformation so that the principal value direction of permeability rotates.This phenomenon leads to a variation in the water seepage direction in typical water-injection applications,thereby hindering the expected exploitation effect of the original well network.Overall,the research findings in this paper can be used as guidance to improve the effectiveness of water injection exploitation in the oil field industry.展开更多
Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity ...Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity numerical simulation data.This presents a significant challenge because the sole source of authentic wellbore production data for training is sparse.In response to this challenge,this work introduces a novel architecture called physics-informed neural network based on domain decomposition(PINN-DD),aiming to effectively utilize the sparse production data of wells for reservoir simulation with large-scale systems.To harness the capabilities of physics-informed neural networks(PINNs)in handling small-scale spatial-temporal domain while addressing the challenges of large-scale systems with sparse labeled data,the computational domain is divided into two distinct sub-domains:the well-containing and the well-free sub-domain.Moreover,the two sub-domains and the interface are rigorously constrained by the governing equations,data matching,and boundary conditions.The accuracy of the proposed method is evaluated on two problems,and its performance is compared against state-of-the-art PINNs through numerical analysis as a benchmark.The results demonstrate the superiority of PINN-DD in handling large-scale reservoir simulation with limited data and show its potential to outperform conventional PINNs in such scenarios.展开更多
In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neura...In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.展开更多
The on-board diagnosis network is the nervous system of high-speed Maglev trains, connecting all controller sensors, and corresponding devices to realize the information acquisition and control. In order to study the ...The on-board diagnosis network is the nervous system of high-speed Maglev trains, connecting all controller sensors, and corresponding devices to realize the information acquisition and control. In order to study the on-board diagnosis network＇s security and reliability, a simulation model for the on-board diagnosis network of high-speed Maglev trains with the optimal network engineering tool （OPNET） was built to analyze the network＇s performance, such as response error and bit error rate on the network load, throughput, and node-state response. The simulation model was verified with an actual on-board diagnosis network structure. The results show that the model results obtained are in good agreement with actual system performance and can be used to achieve actual communication network optimization and control algorithms.展开更多
Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to dem...Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to demonstrate the feasibility of producing a tool that can provide a performance evaluation of various eonstellation access techniques and routing policies. The architecture and design of the simulation system are explored. The algorithm of data routing and instrument scheduling in this project is described. Besides these, the key methodologies of simulating the inter-satellite link features in the data transmissions are also discussed. The performance of both instrument scheduling algorithm and routing schemes is evaluated and analyzed through extensive simulations under a typical scenario.展开更多
A controllable hydrostatic thrust bearing was presented to improve rigidity. The bearing worktable poses were controlled by coupling oilfilm thickness of four controllable chambers. The chamber flow can be regulated b...A controllable hydrostatic thrust bearing was presented to improve rigidity. The bearing worktable poses were controlled by coupling oilfilm thickness of four controllable chambers. The chamber flow can be regulated by electro hydraulic servo valve-control variable pump according to the surface roughness, load, cutting force, and thermal effects of worktable. The mathematical models of the controllable chamber flow, servo variable mechanism and controller were built. The pose control model was established, which contained the kinematics positive and negative solution and control strategy of feedforward and hydraulic cylinder position feedback. Hardware-in-loop simulation experiment was carried out on the electro hydraulic servo test bench by means of the non-linear relation of film thickness and hydraulic cylinder displacement. Hardware-in-loop simulation experiment results show that the controllable bearings exhibit high oilfilm rigidity, the rising time is 0.24 s and the maximum overshoot is 2.23%, and can be applied in high precision heavy machine tool.展开更多
基金Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2023C01National Natural Science Foundation of China under Grant No.52478570Distinguished Young Scholars Program of the Natural Science Foundation of Heilongjiang Province,China under Grant No.JQ2024E002。
文摘The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismic wave simulation with a data-constrained finite-fault rupture model.The constraint is implemented by identifying the optimal ground motion models(GMMs)through a scoring system that selects the best-fit GMMs to mid-and far-field China Earthquake Networks Center(CENC)seismic network data;and applying the optimal GMMs to refine the rupture model parameters for near-fault intensity field simulation.The simulated near-fault seismic intensity field reproduces seismic intensities collected from Myanmar’s sparse seismic network and concentrated in≥Ⅷintensity zones within 50 km of the projected fault plane;and identifies abnormal intensity regions exhibiting≥Ⅹintensity along the Meiktila-Naypyidaw corridor and near Shwebo that are attributed to soft soil amplification effects and near-fault directivity.This framework can also be applied to post-earthquake assessments in other similar regions.
基金Funded by the National Key Research and Development Program(2022YFC3003502).
文摘This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,particularly suited for regions with limited seismic data.Herein,we report a generative adversarial network(GAN)framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records.The constructed GAN model generates ground motions based on continuous physical variables such as source distance,site conditions,and magnitude,effectively capturing the complexity and diversity of ground motions under different scenarios.This capability allows the proposed model to approximate real seismic data,making it applicable to a wide range of engineering purposes.Using the Shandong Pingyuan earthquake as an example,a specialized dataset was constructed based on regional real ground motion records.The response spectrum at target locations was obtained through inverse distance-weighted interpolation of actual response spectra,followed by continuous wavelet transform to derive the ground motion time histories at these locations.Through iterative parameter adjustments,the constructed GAN model learned the probability distribution of strong-motion data for this event.The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics,accurately reflecting the non-stationary nature of seismic records.Statistical comparisons between synthetic and real response spectra,waveform envelopes,and peak ground acceleration show a high degree of similarity,underscoring the effectiveness of the model in replicating both the statistical and physical characteristics of real ground motions.These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions,providing a reliable approach for enriching regional ground motion databases.Additionally,the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.
基金supported by National Natural Science Foundation of China(52274323 and 524743495)the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240231.
文摘In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.
基金Project supported by the National Natural Science Foundation of China(Nos.62266030 and 61863025)。
文摘The diversity and complexity of the user population on the campus network increase the risk of computer virus infection during terminal information interactions.Therefore,it is crucial to explore how computer viruses propagate between terminals in such a network.In this study,we establish a novel computer virus spreading model based on the characteristics of the basic network structure and a classical epidemic-spreading dynamics model,adapted to real-world university scenarios.The proposed model contains six groups:susceptible,unisolated latent,isolated latent,infection,recovery,and crash.We analyze the proposed model's basic reproduction number and disease-free equilibrium point.Using real-world university terminal computer virus propagation data,a basic computer virus infection rate,a basic computer virus removal rate,and a security protection strategy deployment rate are proposed to define the conversion probability of each group and perceive each group's variation tendency.Furthermore,we analyze the spreading trend of computer viruses in the campus network in terms of the proposed computer virus spreading model.We propose specific measures to suppress the spread of computer viruses in terminals,ensuring the safe and stable operation of the campus network terminals to the greatest extent.
基金funded by the SINOPEC Science and Technology Project(No.P18080).
文摘The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.
文摘Seismic quantitative reservoir simulations and characterizations have played a vital role in exploring stratigraphic traps,such as lateaggradational lowstands prograding wedge systems(LPWS)within lowstands systems tracts(LST).However,seismic data acquisition operations are always dominated by exceptional seismic coherent noise events,e.g.,multiples,which reduce the signal strengths of the sourcegenerated incident seismic waves within vertically and laterally heterogeneous earth systems.Hence,these noise events create hurdles in predicting paleo-depositional impedance(PDI),paleo-thickness(PTS),paleo-dense fractured networks,erosional and depositional zones,faultcontrolled migrations,and types of seismic reflection configurations(SRFC),which are key elements in developing stratigraphic pinch-out traps.This research utilizes the state-of-the-art technologies of spectral wavelet-based instantaneous time-frequency analysis and seismic waveform frequency-controlled porosity-constrained static reservoir simulation(FDPVS)tools to quantify the LPWS inside the Onshore Basin,Pakistan.The use of conventional amplitude-based seismic attributes,such as the average energy,remained a better tool for deciphering the overall geological architecture of the LPWS.Conventional FDPVS realizations resolved a PDI of−1.391 gm./c.c.^(*)m/s to−0.97 gm./c.c.^(*)m/s for LPWS with PTS of 12 and 20 m,respectively.A 0.9 km lateral extent of paleo-dense fractured networks(PDFN)with a strong linear regression R^(2)=0.93 was also resolved.Average energy attribute-based instantaneous frequency FDPVS realizations enabled the imaging of parallel-toprograding SRFC with resolved magnitudes of−0.259 gm./c.c.^(*)m/s for PDI,20 m for PTS,and 0.73 km for PDFN with linear regression transforms at R^(2)=0.92,which indicates the deposition of onlap fill facies inside the LPWS during extensive sea-level fall.These realizations have also resolved frequency-controlled fault migrations on 27-Hz spectral waveform-based amplitude plots with 2.174 gm./c.c.^(*)m/s PDI for conduit fault systems and 27-Hz with 0.585 gm./c.c.^(*)m/s PDI for sealing fault systems.All these structural configurations are completely sealed up by transgressive seals of transgressive systems tracts and,hence,developed into pure stratigraphic-based oil and gas plays.This research has strong implications for side-tracking drilling locations and provides an analogue for basins with similar geology and stratigraphy worldwide.
基金supported by the Hubei Province Research Innovation Team Project(T2021022)Scientific Research Projects of Hubei Health Commission(WJ2023M119).
文摘Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCards,Online Mendelian Inheritance in Man,Gene Expression Omnibus,and Comparative Toxicogenomics Database,the intersection core targets of CUR and diabetic retinopathy were identified.The intersection target was imported into the STRING database to obtain the protein-protein interaction map.According to the Database for Annotation,Visualization and Integrated Discovery database,the intersected targets were enriched in Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways.Then Cytoscape 3.9.1 is used to make the drug-target-disease-pathway network.The mechanism of CUR and diabetic retinopathy was further verified by molecular docking and molecular dynamics simulation.Results:There were 203 intersecting targets of CUR and diabetic retinopathy identified.1320 GO entries were enriched for GO functions,which were primarily involved in the composition of cells such as identical protein binding,protein binding,enzyme binding,etc.It was found that 175 pathways were enriched using Kyoto Encyclopedia of Genes and Genomes pathway enrichment methods,which were mainly included in the lipid and atherosclerosis,AGE-RAGE signaling pathway in diabetic complications,pathways in cancer,etc.In the molecular docking analysis,CUR was found to have a good ability to bind to the core targets of albumin,IL-1B,and IL-6.The binding of albumin to CUR was further verified by molecular dynamics simulation.Conclusion:As a result of this study,CUR may exert a role in the treatment of diabetic retinopathy through multi-target and multi-pathway regulation,which indicates a possible direction of future research.
文摘The simulation techniques of hardware-in-loop simulation(HLS) of homing antitank missile based on the personal computer (PC) are discussed. The PC and MCS-96 chip controller employ A/D and D/A boards (with photoelectricity isolation) to transfer measur ment and control information about homing head, gyro and rudder and utilize the digital hand shaking board to build correct communication communication protocol. In order to satisfy the real-time requirement of HLS, this paper first simplifies the aerodynamic data file reasonably, then builds a PC software with C language. The program of the controller part is made with PL/M language. The simulation of HLS based on PC is done with the same sampling period of 10ms as that of YH-F1 and the experiment results are identical to those of digital simulation of the homing anti-tank guided missile.
基金supported by the National Natural Science Foundation of China(No.92370118)the National Science Fund for Excellent Young Scientist Fund Program(Overseas)of China,the Science and Technology Activities Fund for Overseas Researchers of Shaanxi Province,China,and the Research Fund of the State Key Laboratory of Solidification Proceeding(NPU)of China(No.2020-QZ-03).
文摘Cadmium selenide(CdSe)is an inorganic semiconductor with unique optical and electronic properties that make it useful in various applications,including solar cells,light-emitting diodes,and biofluorescent tagging.In order to synthesize high-quality crystals and subsequently integrate them into devices,it is crucial to understand the atomic scale crystallization mechanism of CdSe.Unfortunately,such studies are still absent in the literature.To overcome this limitation,we employed an enhanced sampling-accelerated active learning approach to construct a deep neural potential with ab initio accuracy for studying the crystallization of CdSe.Our brute-force molecular dynamics simulations revealed that a spherical-like nu-cleus formed spontaneously and stochastically,resulting in a stacking disordered structure where the competition between hexagonal wurtzite and cubic zinc blende polymorphs is temperature-dependent.We found that pure hexagonal crystal can only be obtained approximately above 1430 K,which is 35 K below its melting temperature.Furthermore,we observed that the solidification dynamics of Cd and Se atoms were distinct due to their different diffusion coefficients.The solidification process was initiated by lower mobile Se atoms forming tetrahedral frameworks,followed by Cd atoms occupying these tetra-hedral centers and settling down until the third-shell neighbor of Se atoms sited on their lattice posi-tions.Therefore,the medium-range ordering of Se atoms governs the crystallization process of CdSe.Our findings indicate that understanding the complex dynamical process is the key to comprehending the crystallization mechanism of compounds like CdSe,and can shed lights in the synthesis of high-quality crystals.
基金financially supported by the National Natural Science Foundation of China(Nos.11102139 and 11472195)the Natural Science Foundation of Hubei Province of China(No.2014CFB713)
文摘The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied by molecular dynamics （MD） simulation. It was found that a closed three-dimensional misfit dislocation network appears on the γ/γ＇ phase interfaces, and the shape of the dislocation network is independent of the lattice mismatch. Under the influence of the temperature, the dislocation network gradually becomes irregular, a/2 [110] dislocations in the γ matrix phase emit and partly cut into the γ＇ phase with the increase in temperature. The dislocation evolution is related to the local stress field, a peak stress occurs at γ/γ＇ phase interface, and with the increase in temperature and relaxation times, the stress in the γ phase gradually increases, the number of dislocations in the γ phase increases and cuts into γ＇ phase from the interfaces where dislocation network is damaged. The results provide important information for understanding the temperature dependence of the dislocation evolution and mechanical properties of Ni-based single-crystal superalloys.
文摘The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).
文摘In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.
基金Project(51008229)supported by the National Natural Science Foundation of ChinaProject supported by Key Laboratory of Road and Traffic Engineering of Tongji University,China
文摘A simulation model was proposed to investigate the relationship between train delays and passenger delays and to predict the dynamic passenger distribution in a large-scale rail transit network. It was assumed that the time varying original-destination demand and passenger path choice probability were given. Passengers were assumed not to change their destinations and travel paths after delay occurs. CapaciW constraints of train and queue rules of alighting and boarding were taken into account. By using the time-driven simulation, the states of passengers, trains and other facilities in the network were updated every time step. The proposed methodology was also tested in a real network, for demonstration. The results reveal that short train delay does not necessarily result in passenger delays, while, on the contrary, some passengers may get benefits from the short delay. However, large initial train delay may result in not only knock-on train and passenger delays along the same line, but also the passenger delays across the entire rail transit network.
文摘This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipeline systems (CFD-simulator). The approach used in CFD-simulators for modeling gas mixture transmission through long, branched, multi-section pipelines is based on tailoring the full system of fluid dynamics equations to conditions of unsteady, non-isothermal processes of the gas mixture flow. Identification, in a CFD-simulator, of safe parameters for gas transmission through compressor stations amounts to finding the interior points of admissible sets described by systems of nonlinear algebraic equalities and inequalities. Such systems of equalities and inequalities comprise a formal statement of technological, design, operational and other constraints to which operation of the network equipment is subject. To illustrate the practicability of the method of numerical simulation of a gas transmission network, we compare computation results and gas flow parameters measured on-site at the gas transmission enter-prise.
基金This work is financially supported by the National Natural Science Foundation Project(No.51374222)National Major Project(No.2017ZX05032004-002)+2 种基金the National Key Basic Research&Development Program(No.2015CB250905)CNPC’s Major Scientific and Technological Project(No.2017E-0405)SINOPEC Major Scientific Research Project(No.P18049-1).
文摘Stress sensitivity is a very important index to understand the seepage characteristics of a reservoir.In this study,dedicated experiments and theoretical arguments based on the visualization of porous media are used to assess the effects of the fracture angle,spacing,and relevant elastic parameters on the principal value of the permeability tensor.The fracture apertures at different angles show different change rates,which influence the relative permeability for different sets of fractures.Furthermore,under the same pressure condition,the fractures with different angles show different degrees of deformation so that the principal value direction of permeability rotates.This phenomenon leads to a variation in the water seepage direction in typical water-injection applications,thereby hindering the expected exploitation effect of the original well network.Overall,the research findings in this paper can be used as guidance to improve the effectiveness of water injection exploitation in the oil field industry.
基金funded by the National Natural Science Foundation of China(Grant No.52274048)Beijing Natural Science Foundation(Grant No.3222037)+1 种基金the CNPC 14th Five-Year Perspective Fundamental Research Project(Grant No.2021DJ2104)the Science Foundation of China University of Petroleum-Beijing(No.2462021YXZZ010).
文摘Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity numerical simulation data.This presents a significant challenge because the sole source of authentic wellbore production data for training is sparse.In response to this challenge,this work introduces a novel architecture called physics-informed neural network based on domain decomposition(PINN-DD),aiming to effectively utilize the sparse production data of wells for reservoir simulation with large-scale systems.To harness the capabilities of physics-informed neural networks(PINNs)in handling small-scale spatial-temporal domain while addressing the challenges of large-scale systems with sparse labeled data,the computational domain is divided into two distinct sub-domains:the well-containing and the well-free sub-domain.Moreover,the two sub-domains and the interface are rigorously constrained by the governing equations,data matching,and boundary conditions.The accuracy of the proposed method is evaluated on two problems,and its performance is compared against state-of-the-art PINNs through numerical analysis as a benchmark.The results demonstrate the superiority of PINN-DD in handling large-scale reservoir simulation with limited data and show its potential to outperform conventional PINNs in such scenarios.
文摘In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.
基金supported by the National Natural Science Foundation of China (No. 51007074)the Program for New Century Excellent Talents in University(NECT-08-0825)+1 种基金the Research and Development Project of the National Railway Ministry (2011J016-B)The basic research universities special fund operations(SWJTU11CX141)
文摘The on-board diagnosis network is the nervous system of high-speed Maglev trains, connecting all controller sensors, and corresponding devices to realize the information acquisition and control. In order to study the on-board diagnosis network＇s security and reliability, a simulation model for the on-board diagnosis network of high-speed Maglev trains with the optimal network engineering tool （OPNET） was built to analyze the network＇s performance, such as response error and bit error rate on the network load, throughput, and node-state response. The simulation model was verified with an actual on-board diagnosis network structure. The results show that the model results obtained are in good agreement with actual system performance and can be used to achieve actual communication network optimization and control algorithms.
基金This project was supported by the National "863" High-Tech Research and Development Program of China(2002AA7170)
文摘Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to demonstrate the feasibility of producing a tool that can provide a performance evaluation of various eonstellation access techniques and routing policies. The architecture and design of the simulation system are explored. The algorithm of data routing and instrument scheduling in this project is described. Besides these, the key methodologies of simulating the inter-satellite link features in the data transmissions are also discussed. The performance of both instrument scheduling algorithm and routing schemes is evaluated and analyzed through extensive simulations under a typical scenario.
基金Project(20050214001) supported by Doctor Foundation of Education Ministry of ChinaProject(GC05A512) and supported by the Program of Heilongjiang Province Science and Technology, ChinaProject(zjg0702-01) supported by the Natural Science Foundation of Heilongjiang Province, China
文摘A controllable hydrostatic thrust bearing was presented to improve rigidity. The bearing worktable poses were controlled by coupling oilfilm thickness of four controllable chambers. The chamber flow can be regulated by electro hydraulic servo valve-control variable pump according to the surface roughness, load, cutting force, and thermal effects of worktable. The mathematical models of the controllable chamber flow, servo variable mechanism and controller were built. The pose control model was established, which contained the kinematics positive and negative solution and control strategy of feedforward and hydraulic cylinder position feedback. Hardware-in-loop simulation experiment was carried out on the electro hydraulic servo test bench by means of the non-linear relation of film thickness and hydraulic cylinder displacement. Hardware-in-loop simulation experiment results show that the controllable bearings exhibit high oilfilm rigidity, the rising time is 0.24 s and the maximum overshoot is 2.23%, and can be applied in high precision heavy machine tool.
