Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

A multi-source data fusion modeling method for debris flow prevention engineering

The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.

Flow field fusion simulation method based on model features and its application in CRDM

The control rod drive mechanism(CRDM)is an essential part of the control and safety protection system of pressurized water reactors.Current CRDM simulations are mostly performed collectively using a single method,ignoring the influence of multiple motion units and the differences in various features among them,which strongly affect the efficiency and accuracy of the simulations.In this study,we constructed a flow field fusion simulation method based on model features by combining key motion unit analysis and various simulation methods and then applied the method to the CRDM simulation process.CRDM performs motion unit decomposition through the structural hierarchy of function-movement-action method,and the key meta-actions are identified as the nodes in the flow field simulation.We established a fused feature-based multimethod simulation process and processed the simulation methods and data according to the features of the fluid domain space and the structural complexity to obtain the fusion simulation results.Compared to traditional simulation methods and real measurements,the simulation method provides advantages in terms of simulation efficiency and accuracy.

MHD Stability Analysis and Flow Controls of Liquid Metal Free Surface Film Flows as Fusion Reactor PFCs

Numerical and experimental investigation results on the magnetohydrodynamics（MHD） film flows along flat and curved bottom surfaces are summarized in this study. A simplified modeling has been developed to study the liquid metal MHD film state, which has been validated by the existing experimental results. Numerical results on how the inlet velocity（V）, the chute width（W） and the inlet film thickness（d0） affect the MHD film flow state are obtained. MHD stability analysis results are also provided in this study. The results show that strong magnetic fields make the stable V decrease several times compared to the case with no magnetic field,especially small radial magnetic fields（Bn） will have a significant impact on the MHD film flow state. Based on the above numerical and MHD stability analysis results flow control methods are proposed for flat and curved MHD film flows. For curved film flow we firstly proposed a new multi-layers MHD film flow system with a solid metal mesh to get the stable MHD film flows along the curved bottom surface. Experiments on flat and curved MHD film flows are also carried out and some firstly observed results are achieved.

A brief review：experimental investigation of zonal flows and geodesic acoustic modes in fusion plasmas

Zonal flows self-generated by turbulence play an important role in regulating turbulence,reducing transport level,and thus improve plasma confinement in fusion plasmas.The zonal flows and geodesic acoustic modes have been identified in various devices.The related issues,such as the poloidal and toroidal symmetries,coupling to turbulence,effects on turbulence and transport,nonlinear energy transfer between turbulence and zonal flows,dependence of the plasma parameters,roles in the confinement regime transitions etc are overviewed briefly in this paper.The interaction between zonal flows and magnetic islands is emphasized.

Research on simulation of gun muzzle flow field empowered by artificial intelligence

Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is proposed.First,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal value.Then physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation laws.Through two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and expansion.The proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is divided.It is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous area.Comparison with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation accuracy.The proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.

The deep spatiotemporal network with dual-flow fusion for video-oriented facial expression recognition

The video-oriented facial expression recognition has always been an important issue in emotion perception.At present,the key challenge in most existing methods is how to effectively extract robust features to characterize facial appearance and geometry changes caused by facial motions.On this basis,the video in this paper is divided into multiple segments,each of which is simultaneously described by optical flow and facial landmark trajectory.To deeply delve the emotional information of these two representations,we propose a Deep Spatiotemporal Network with Dual-flow Fusion(defined as DSN-DF),which highlights the region and strength of expressions by spatiotemporal appearance features and the speed of change by spatiotemporal geometry features.Finally,experiments are implemented on CKþand MMI datasets to demonstrate the superiority of the proposed method.

Adaptation analysis and fusion correction method of CMIP6 precipitation simulation data on the Qinghai-Tibetan Plateau

In order to obtain more accurate precipitation data and better simulate the precipitation on the Tibetan Plateau,the simulation capability of 14 Coupled Model Intercomparison Project Phase 6(CMIP6)models of historical precipitation(1982-2014)on the Qinghai-Tibetan Plateau was evaluated in this study.Results indicate that all models exhibit an overestimation of precipitation through the analysis of the Taylor index,temporal and spatial statistical parameters.To correct the overestimation,a fusion correction method combining the Backpropagation Neural Network Correction(BP)and Quantum Mapping(QM)correction,named BQ method,was proposed.With this method,the historical precipitation of each model was corrected in space and time,respectively.The correction results were then analyzed in time,space,and analysis of variance(ANOVA)with those corrected by the BP and QM methods,respectively.Finally,the fusion correction method results for each model were compared with the Climatic Research Unit(CRU)data for significance analysis to obtain the trends of precipitation increase and decrease for each model.The results show that the IPSL-CM6A-LR model is relatively good in simulating historical precipitation on the Qinghai-Tibetan Plateau(R=0.7,RSME=0.15)among the uncorrected data.In terms of time,the total precipitation corrected by the fusion method has the same interannual trend and the closest precipitation values to the CRU data;In terms of space,the annual average precipitation corrected by the fusion method has the smallest difference with the CRU data,and the total historical annual average precipitation is not significantly different from the CRU data,which is better than BP and QM.Therefore,the correction effect of the fusion method on the historical precipitation of each model is better than that of the QM and BP methods.The precipitation in the central and northeastern parts of the plateau shows a significant increasing trend.The correlation coefficients between monthly precipitation and site-detected precipitation for all models after BQ correction exceed 0.8.

Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

Towards implementation of alloy-specific thermo-fluid modelling for laser powder-bed fusion of Mg alloys

Multi-physics thermo-fluid modeling has been extensively used as an approach to understand melt pool dynamics and defect formation as well as optimizing the process-related parameters of laser powder-bed fusion(L-PBF).However,its capabilities for being implemented as a reliable tool for material design,where minor changes in material-related parameters must be accurately captured,is still in question.In the present research,first,a thermo-fluid computational fluid dynamics(CFD)model is developed and validated against experimental data.Considering the predicted material properties of the pure Mg and commercial ZK60 and WE43 Mg alloys,parametric studies are done attempting to elucidate how the difference in some of the material properties,i.e.,saturated vapor pressure,viscosity,and solidification range,can influence the melt pool dynamics.It is found that a higher saturated vapor pressure,associated with the ZK60 alloy,leads to a deeper unstable keyhole,increasing the keyhole-induced porosity and evaporation mass loss.Higher viscosity and wider solidification range can increase the non-uniformity of temperature and velocity distribution on the keyhole walls,resulting in increased keyhole instability and formation of defects.Finally,the WE43 alloy showed the best behavior in terms of defect formation and evaporation mass loss,providing theoretical support to the extensive use of this alloy in L-PBF.In summary,this study suggests an approach to investigate the effect of materials-related parameters on L-PBF melting and solidification,which can be extremely helpful for future design of new alloys suitable for L-PBF.

Spatial-temporal distribution of debris flow impact pressure on rigid barrier

Grain composition plays a vital role in impact pressure of debris flow. Current approaches treat debris flow as uniform fluid and almost ignore its granular effects. A series of flume experiments have been carried out to explore the granular influence on the impact process of debris flow by using a contact surface pressure gauge sensor(Tactilus~?, produced by Sensor Products LLC). It is found that the maximum impact pressure for debris flow of low density fluctuates drastically with a long duration time while the fluctuation for flow of high density is short in time, respectively presenting logarithmic and linear form in longitudinal attenuation. This can be ascribed to the turbulence effect in the former and grain collisions and grainfluid interaction in the latter. The horizontal distribution of the impact pressure can be considered as the equivalent distribution. For engineering purposes, the longitudinal distribution of the pressure can be generalized to a triangular distribution, from which a new impact method considering granular effects is proposed.

Short-term travel flow prediction method based on FCM-clustering and ELM

Short-term travel flow prediction has been the core of the intelligent transport systems(ITS). An advanced method based on fuzzy C-means(FCM) and extreme learning machine(ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function, this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.

DuFNet:Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology.Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis.Semantic segmentation is also a challenging technology for image understanding and scene parsing.We focused on the challenging task of real-time semantic segmentation in this paper.In this paper,we proposed a novel fast architecture for real-time semantic segmentation named DuFNet.Starting from the existing work of Bilateral Segmentation Network(BiSeNet),DuFNet proposes a novel Semantic Information Flow(SIF)structure for context information and a novel Fringe Information Flow(FIF)structure for spatial information.We also proposed two kinds of SIF with cascaded and paralleled structures,respectively.The SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature fusionmodule.Features from previous stages usually contain rich low-level details but high-level semantics for later stages.Themultiple convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational cost.The FIF consists of a pooling layer and an upsampling operator followed by projection convolution layer.The concise component provides more spatial details for the network.Compared with BiSeNet,our work achieved faster speed and comparable performance with 72.34%mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.

Investigation of the Laser Powder Bed Fusion Process of Ti-6.5Al-3.5Mo-1.5Zr-0.3Si Alloy

Laser powder bed fusion(LPBF)is an advanced manufacturing technology;however,inappropriate LPBF process parameters may cause printing defects in materials.In the present work,the LPBF process of Ti-6.5Al-3.5Mo-1.5Zr-0.3Si alloy was investigated by a two-step optimization approach.Subsequently,heat transfer and liquid flow behaviors during LPBF were simulated by a well-tested phenomenological model,and the defect formation mechanisms in the as-fabricated alloy were discussed.The optimized process parameters for LPBF were detected as laser power changed from 195 W to 210 W,with scanning speed of 1250 mm/s.The LPBF process was divided into a laser irradiation stage,a spreading flow stage,and a solidification stage.The morphologies and defects of deposited tracks were affected by liquid flow behavior caused by rapid cooling rates.The findings of this research can provide valuable support for printing defect-free metal components.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

Competition between fusion-evaporation and multifragmentation in central collisions in ^(58)Ni+^(48)Ca at 25A MeV

The experimental data concerning the58Ni+48Ca reaction at Elab(Ni)=25A MeV,collected by using the CHIMERA 4π device,have been analyzed in order to investigate the competition among different reaction mechanisms for central collisions in the Fermi energy domain.As a main criterion for centrality selection we have chosen the flow angle(flow) method,making an event-by-event analysis that considers the shape of events,as it is determined by the eigenvectors of the experimental kinetic-energy tensor.For the selected central events(flow >60°) some global variables,good to characterize the pattern of central collisions have been constructed.The main features of the reaction products were explored by using different constraints on some of the relevant observables,like mass and velocity distributions and their correlations.Much emphasis was devoted,for central collisions,to the competition between fusion-evaporation processes with subsequent identification of a heavy residue and a possible multifragmentation mechanism of a well defined(if any) transient nuclear system.Dynamical evolution of the system and pre-equilibrium emission were taken into account by simulating the reactions in the framework of transport theories.Different approaches have been envisaged(dynamical stochastic BNV calculations + sequential SIMON code,QMD,CoMD,etc.).Preliminary comparison of the experimental data with BNV calculations shows reasonable agreement with the assumption of sequential multifragmentation emission in the mass region of IMFs close to the heavy residues.Possible deviations from sequential processes were found for those IMFs in the region of masses intermediate between the mass of heavy residues and the mass of light IMFs.Further simulations are in progress.The experimental analysis will be enriched also by information obtained inspecting the IMF-IMF correlation function,in order to elucidated the nature of space-time decay property of the emitting source associated with events having the largest IMF multiplicity.

Study on Flow Unit of Heavy Oil Bottom Water Reservoir with Over-Limited Thickness in Offshore Oilfield

The upper Ming section of L oilfield is a typical offshore heavy oil bottom-water reservoir with thick fluvial layers. All horizontal wells are developed by natural energy. Due to the few drilling holes and influence by the resolution of seismic data, it is difficult to describe reservoirs with thickness over 20 meters. In this paper, seismic resonance amplitude inversion technology is introduced to restore the real response of thick reservoirs and interbeds by drilling and drilling verification, and the geological bodies with different thickness are displayed by frequency division RGB three primary colors. Flow units of heavy oil reservoirs with bottom water are divided according to the three major factors of interlayer, lithologic internal boundary and water-oil thickness ratio which have the greatest influence on horizontal well development, thick sand bodies are divided into 10 different flow units in three levels, each unit is separated from each other, and the reservoir structure, water-cut characteristics and water-flooding characteristics are different. The reliability of the research is improved by using the dynamic data of horizontal wells and newly drilled passing wells, which provides a basis for tapping the potential of heavy oil reservoirs with bottom water.

Application of Clustering Fusion on Foreign Fibers Measure-Control System

According to the structure characteristics of foreign fibers detection system,the foreign fiber flow flux mathematical model and fiber detection system were designed.The information fusion clustering structure of foreign fiber flow flux was put forward.The data of the pressure difference,pressure,temperature,and density sensor which had impacted on flux were integrated and output by the Adaptive Resonance Theory-2(ART-2)network and BP network to clustering analysis of output space.The clustering control strategy will keep the output flow pressure stable,when the output pressure and temperature change.

Flow-based SLAM:From geometry computation to learning

Simultaneous localization and mapping(SLAM)has attracted considerable research interest from the robotics and computer-vision communities for>30 years.With steady and progressive efforts being made,modern SLAM systems allow robust and online applications in real-world scenes.We examined the evolution of this powerful perception tool in detail and noticed that the insights concerning incremental computation and temporal guidance are persistently retained.Herein,we denote this temporal continuity as a flow basis and present for the first time a survey that specifically focuses on the flow-based nature,ranging from geometric computation to the emerging learning techniques.We start by reviewing two essential stages for geometric computation,presenting the de facto standard pipeline and problem formulation,along with the utilization of temporal cues.The recently emerging techniques are then summarized,covering a wide range of areas,such as learning techniques,sensor fusion,and continuous time trajectory modeling.This survey aims at arousing public attention on how robust SLAM systems benefit from a continuously observing nature,as well as the topics worthy of further investigation for better utilizing the temporal cues.

多尺度融合与动态自适应图的公交客流预测模型

公交客流预测是公共交通规划和管理中的重要问题。虽然时空图卷积在地铁客流预测任务中获得了很好的预测效果,但是面对公交更复杂的线路、大规模的节点数据,现有的基于图卷积的空间建模方法将带来巨大的空间内存消耗。同时,公交客流量短时间范围内更可能受到瞬时交通状况的影响。为了解决这些挑战,提出了一种多尺度融合和动态自适应图的公交客流预测模型(MFDAG)。该模型融合客流、时刻和周信息以增加数据的特征维度,用动态自适应图的方法来学习不同站点之间的关系。进一步提出了一种多尺度融合传播的方法来表示复杂的空间依赖关系,同时设计了一种多尺度卷积传播的方法来学习不同尺度的时间依赖关系。在两个真实的客流数据集上进行了实验,并与其他交通预测方法进行了比较。实验结果表明,所提出的多尺度融合和动态自适应图的公交客流预测方法具有更高的预测准确度。