Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

WT-FCTGN:A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.

A new interacting capillary bundle model on the multiphase flow in micropores of tight rocks

Surfactants are widely used in the fracturing fluid to enhance the imbibition and thus the oil recovery rate. However, current numerical models cannot capture the physics behind capillary imbibition during the wettability alteration by surfactants. Although the interacting capillary bundle(ICB) model shows potential in characterizing imbibition rates in different pores during wettability alteration, the existing ICB models neglect the influence of wettability and viscosity ratio on the imbibition behavior, making it difficult to accurately describe the oil-water imbibition behavior within the porous media. In this work,a new ICB mathematical model is established by introducing pressure balance without assuming the position of the leading front to comprehensively describe the imbibition behavior in a porous medium under different conditions, including gas-liquid spontaneous imbibition and oil-water imbibition.When the pore size distribution of a tight rock is known, this new model can predict the changes of water saturation during the displacement process in the tight rock, and also determine the imbibition rate in pores of different sizes. The water saturation profiles obtained from the new model are validated against the waterflooding simulation results from the CMG, while the imbibition rates calculated by the model are validated against the experimental observations of gas-liquid spontaneous imbibition. The good match above indicates the newly proposed model can show the water saturation profile at a macroscopic scale while capture the underlying physics of the multiphase flow in a porous medium at a microscopic scale. Simulation results obtained from this model indicate that both wettability and viscosity ratio can affect the sequence of fluid imbibition into pores of different sizes during the multiphase flow, where less-viscous wetting fluid is preferentially imbibed into larger pores while more-viscous wetting fluid tends to be imbibed into smaller pores. Furthermore, this model provides an avenue to calculate the imbibition rate in pores of different sizes during wettability alteration and capture the non-Darcy effect in micro-and nano-scale pores.

Dynamic-based model for calculating the boulder impact force in debris flow

The boulder impact force in debris flow is generally calculated by static methods such as the cantilever beam models.However,these methods cannot describe the dynamic scenario of boulder collision on structures,so the inertia and damping effects of the structures are not involved causing an overestimation on the boulder impact force.In order to address this issue,a dynamic-based model for calculating the boulder impact force of a debris flow was proposed in this study,and the dynamic characteristics of a cantilever beam with multiple degrees of freedom under boulder collision were investigated.By using the drop-weight method to simulate boulders within debris flow,seven experiments of drop-weight impacting the cantilever beam were used to calibrate the error of the dynamicbased model.Results indicate that the dynamic-based model is able to reconstruct the impact force history on the cantilever beam during impact time and the error of dynamic-based model is 15.3%in calculating boulder impact force,significantly outperforming the cantilever beam model’s error of 285%.Therefore,the dynamic-based model can overcome the drawbacks of the static-based models and provide a more reliable theoretical foundation for the engineering design of debris flow control structures.

An extended social force model on unidirectional flow considering psychological and behavioral impacts of hazard source

An accurate assessment of the evacuation efficiency in case of disasters is of vital importance to the safety design of buildings and street blocks.Hazard sources not only physically but psychologically affect the pedestrians,which may further alter their behavioral patterns.This effect is especially significant in narrow spaces,such as corridors and alleys.This study aims to integrate a non-spreading hazard source into the social force model following the results from a previous experiment and simulation,and to simulate unidirectional pedestrian flows over various crowd densities and clarity–intensity properties of the hazard source.The integration include a virtual repulsion force from the hazard source and a decay on the social force term.The simulations reveal(i)that the hazard source creates virtual bottlenecks that suppress the flow,(ii)that the inter-pedestrian push forms a stabilisation phase on the flow-density curve within medium-to-high densities,and(iii)that the pedestrians are prone to a less orderly and stable pattern of movement in low clarity–intensity scenarios,possibly with lateral collisions passing the hazard source.

A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

Modeling of multiphase flow in low permeability porous media:Effect of wettability and pore structure properties

Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the effects of complex pore structures and wettability.To address this issue,based on the digital rock of low permeability sandstone,a direct numerical simulation is performed considering the interphase drag and boundary slip to clarify the microscopic water-oil displacement process.In addition,a dual-porosity pore network model(PNM)is constructed to obtain the water-oil relative permeability of the sample.The displacement efficiency as a recovery process is assessed under different wetting and pore structure properties.Results show that microscopic displacement mechanisms explain the corresponding macroscopic relative permeability.The injected water breaks through the outlet earlier with a large mass flow,while thick oil films exist in rough hydrophobic surfaces and poorly connected pores.The variation of water-oil relative permeability is significant,and residual oil saturation is high in the oil-wet system.The flooding is extensive,and the residual oil is trapped in complex pore networks for hydrophilic pore surfaces;thus,water relative permeability is lower in the water-wet system.While the displacement efficiency is the worst in mixed-wetting systems for poor water connectivity.Microporosity negatively correlates with invading oil volume fraction due to strong capillary resistance,and a large microporosity corresponds to low residual oil saturation.This work provides insights into the water-oil flow from different modeling perspectives and helps to optimize the development plan for enhanced recovery.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Stochastic Analysis and Modeling of Velocity Observations in Turbulent Flows

Highly turbulent water flows,often encountered near human constructions like bridge piers,spillways,and weirs,display intricate dynamics characterized by the formation of eddies and vortices.These formations,varying in sizes and lifespans,significantly influence the distribution of fluid velocities within the flow.Subsequently,the rapid velocity fluctuations in highly turbulent flows lead to elevated shear and normal stress levels.For this reason,to meticulously study these dynamics,more often than not,physical modeling is employed for studying the impact of turbulent flows on the stability and longevity of nearby structures.Despite the effectiveness of physical modeling,various monitoring challenges arise,including flow disruption,the necessity for concurrent gauging at multiple locations,and the duration of measurements.Addressing these challenges,image velocimetry emerges as an ideal method in fluid mechanics,particularly for studying turbulent flows.To account for measurement duration,a probabilistic approach utilizing a probability density function(PDF)is suggested to mitigate uncertainty in estimated average and maximum values.However,it becomes evident that deriving the PDF is not straightforward for all turbulence-induced stresses.In response,this study proposes a novel approach by combining image velocimetry with a stochastic model to provide a generic yet accurate description of flow dynamics in such applications.This integration enables an approach based on the probability of failure,facilitating a more comprehensive analysis of turbulent flows.Such an approach is essential for estimating both short-and long-term stresses on hydraulic constructions under assessment.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Particulate flow modelling in a spiral separator by using the Eulerian multi-fluid VOF approach

The Euler-Euler model is less effective in capturing the free surface of flow film in the spiral separator,and thus a Eulerian multi-fluid volume of fluid(VOF)model was first proposed to describe the particulate flow in spiral separators.In order to improve the applicability of the model in the high solid concentration system,the Bagnold effect was incorporated into the modelling framework.The capability of the proposed model in terms of predicting the flow film shape in a LD9 spiral separator was evaluated via comparison with measured flow film thicknesses reported in literature.Results showed that sharp air–water and air-pulp interfaces can be obtained using the proposed model,and the shapes of the predicted flow films before and after particle addition were reasonably consistent with the observations reported in literature.Furthermore,the experimental and numerical simulation of the separation of quartz and hematite were performed in a laboratory-scale spiral separator.When the Bagnold lift force model was considered,predictions of the grade of iron and solid concentration by mass for different trough lengths were more consistent with experimental data.In the initial development stage,the quartz particles at the bottom of the flow layer were more possible to be lifted due to the Bagnold force.Thus,a better predicted vertical stratification between quartz and hematite particles was obtained,which provided favorable conditions for subsequent radial segregation.

High-precision nonisothermal transient wellbore drift flow model suitable for the full flow pattern domain and full dip range

A reliable multiphase flow simulator is an important tool to improve wellbore integrity and production decision-making.To develop a multiphase flow model with high adaptability and high accuracy,we first build a multiphase flow database with 3561 groups of data and developed a drift closure relationship with stable continuity and high adaptability.Second,a high-order numerical scheme with strong fault capture ability is constructed by effectively combining MUSCL technology,van Albada slope limiter and AUSMV numerical scheme.Finally,the energy equation is coupled into the AUSMV numerical scheme of the drift flow model in the form of finite difference.A transient non-isothermal wellbore multiphase flow model with wide applicability is formed by integrating the three technologies,and the effects of various factors on the calculation accuracy are studied.The accuracy of the simulator is verified by comparing the measurement results with the blowout experiment of a full-scale experimental well.

Characteristics of traumatic brain injury models:from macroscopic blood flow changes to microscopic mitochondrial changes

Controlled cortical impingement is a widely accepted method to induce traumatic brain injury to establish a traumatic brain injury animal model.A strike depth of 1 mm at a certain speed is recommended for a moderate brain injury and a depth of>2 mm is used to induce severe brain injury.However,the different effects and underlying mechanisms of these two model types have not been proven.This study investigated the changes in cerebral blood flow,differences in the degree of cortical damage,and differences in motor function under different injury parameters of 1 and 2 mm at injury speeds of 3,4,and 5 m/s.We also explored the functional changes and mitochondrial damage between the 1 and 2 mm groups in the acute(7 days)and chronic phases(30 days).The results showed that the cerebral blood flow in the injured area of the 1 mm group was significantly increased,and swelling and bulging of brain tissue,increased vascular permeability,and large-scale exudation occurred.In the 2 mm group,the main pathological changes were decreased cerebral blood flow,brain tissue loss,and cerebral vasospasm occlusion in the injured area.Substantial motor and cognitive impairments were found on day 7 after injury in the 2 mm group;at 30 days after injury,the motor function of the 2 mm group mice recovered significantly while cognitive impairment persisted.Transcriptome sequencing showed that compared with the 1 mm group,the 2 mm group expressed more ferroptosis-related genes.Morphological changes of mitochondria in the two groups on days 7 and 30 using transmission electron microscopy revealed that on day 7,the mitochondria in both groups shrank and the vacuoles became larger;on day 30,the mitochondria in the 1 mm group became larger,and the vacuoles in the 2 mm group remained enlarged.By analyzing the proportion of mitochondrial subgroups in different groups,we found that the model mice had different patterns of mitochondrial composition at different time periods,suggesting that the difference in the degree of damage among traumatic brain injury groups may reflect the mitochondrial changes.Taken together,differences in mitochondrial morphology and function between the 1 and 2 mm groups provide a new direction for the accurate classification of traumatic brain injury.Our results provide reliable data support and evaluation methods for promoting the establishment of standard mouse controlled cortical impingement model guidelines.

Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow

Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers.The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point.The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43,with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model(DSM).In a posteriori test,the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles,mean temperature profiles,turbulent intensities,total Reynolds stress,total Reynolds heat flux,and mean SGS flux of kinetic energy,and outperformed the Smagorinsky model.

Numerical simulation of gas–liquid flow in the bubble column using Wray–Agarwal turbulence model coupled with population balance model

In this paper,an improved computational fluid dynamic(CFD)model for gas-liquid flow in bubble column was developed using the one-equation Wary-Agarwal(WA)turbulence model coupled with the population balance model(PBM).Through 18 orthogonal test cases,the optimal combination of interfacial force models,including drag force,lift force,turbulent dispersion force.The modified wall lubrication force model was proposed to improve the predictive ability for hydrodynamic behavior near the wall of the bubble column.The values simulated by optimized CFD model were in agreement with experimental data,and the errors were within±20%.In addition,the axial velocity,turbulent kinetic energy,bubble size distribution,and the dynamic characteristic of bubble plume were analyzed at different superficial gas velocities.This research work could provide a theoretical basis for the extension of the CFD-PBM coupled model to other multiphase reactors..

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

Explicit Correlation Model of Multi-source Constraints for Re-design Parts with Complex Curved Surface

In precision machining of complex curved surface parts with high performance, geometry accuracy is not the only constraint, but the performance should also be met. Performance of this kind of parts is closely related to the geometrical and physical parameters, so the final actual size and shape are affected by multiple source constraints, such as geometry, physics, and performance. These parts are rather difficult to be manufactured and new manufacturing method according to performance requirement is urgently needed. Based on performance and manufacturing requirements for complex curved surface parts, a new classification method is proposed, which divided the complex curved surface parts into two categories： surface re-design complex curved surface parts with multi-source constraints（PRCS） and surface unique complex curved surface parts with pure geometric constraints（PUCS）. A correlation model is constructed between the performance and multi-source constraints for PRCS, which reveals the correlation between the performance and multi-source constraints. A re-design method is also developed. Through solving the correlation model of the typical paws performance-associated surface, the mapping relation between the performance-associated surface and the related removal amount is obtained. The explicit correlation model and the method for the corresponding related removal amount of the performance-associated surface are built based on the classification of surface re-design complex curved surface parts with multi-source constraints. Research results have been used in the actual processing of the typical parts such as radome, common bottom components, nozzle, et al., which shows improved efficiency and accuracy of the precision machining for the surface re-design parts with complex curved surface.

Modeling of liquid film thickness around Taylor bubbles rising in vertical stagnant and co-current slug flowing liquids

The hydrodynamic study of the liquid film around Taylor bubbles in slug flow has great significance for understanding parallel flow and interaction between Taylor bubbles.The prediction models for liquid film thickness mainly focus on stagnant flow,and some of them remain inaccurate performance.However,in the industrial process,the slug flow essentially is co-current flow.Therefore,in this paper,the liquid film thickness is studied by theoretical analysis and experimental methods under two conditions of stagnant and co-current flow.Firstly,under the condition of stagnant flow,the present work is based on Batchelor's theory,and modifies Batchelor's liquid film thickness model,which effectively improves its prediction accuracy.Under the condition of co-current flow,the prediction model of average liquid film thickness in slug flow is established by force and motion analysis.Taylor bubble length is introduced into the model as an important parameter.Dynamic experiments were carried out in the pipe with an inner diameter of 20 mm.The liquid film thickness,Taylor bubble velocity and length were measured by distributed ultrasonic sensor and intrusive cross-correlation conductivity sensor.Comparing the predicted value of the model with the measured results,the relative error is controlled within 10%.

A phenomenological model for plastic flow behavior of rotating band material with a large temperature range

The plastic flow behavior of the rotating band material is investigated in this paper. The rotating band material is processed from H96 brass alloy, which is hardened to a much higher yield strength compared to the annealed one. The dynamically uniaxial compression behavior of the material is tested using the split Hopkinson pressure bar(SHPB) with temperature and strain rate ranging from 297 to 1073 K and500 to 3000 s^(-1), respectively, and a phenomenological plastic flow stress model is developed to describe the mechanical behavior of the material. The material is found to present noticeable temperature sensitivity and weak strain-rate sensitivity. The construction of the plastic flow stress model has two steps. Firstly, three univariate stress functions, taking plastic strain, plastic strain rate and temperature as independent variable, respectively, are proposed by fixing the other two variables. Then, as the three univariate functions describe the special cases of flow stress behavior under various conditions, the principle of stress compatibility is adopted to obtain the complete flow stress function. The numerical results show that the proposed plastic flow stress model is more suitable for the rotating band material than the existing well-known models.

3D Geological Modeling with Multi-source Data Integration in Polymetallic Region:A Case Study of Luanchuan,Henan Province,China

The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luanchuan,the case study area,southwestern Henan Province,is an important molybdenum-tungsten -lead-zinc polymetallic belt in China.