Quickly obtaining densely dispersed coherent particles in steel matrix and its related mechanical property

Densely distributed coherent nanoparticles(DCN)in steel matrix can enhance the work-hardening ability and ductility of steel simultaneously.All the routes to this end can be generally classified into the liquid-solid route and the solid-solid route.However,the formation of DCN structures in steel requires long processes and complex steps.So far,obtaining steel with coherent particle enhancement in a short time remains a bottleneck,and some necessary steps remain unavoidable.Here,we show a high-efficiency liquid-phase refining process reinforced by a dynamic magnetic field.Ti-Y-Mn-O particles had an average size of around(3.53±1.21)nm and can be obtained in just around 180 s.These small nanoparticles were coherent with the matrix,implying no accumulated dislocations between the particles and the steel matrix.Our findings have a potential application for improving material machining capacity,creep resistance,and radiation resistance.

Impact of Pollutant Concentration and Particle Deposition on the Radiative Flow of Casson-Micropolar Fluid between Parallel Plates

Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge concentrations.The waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution levels.This study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle deposition.To explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are considered.The governing equations are transformed into ordinary differential equations with the help of suitable similarity transformations.The Runge-Kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary conditions.The integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of parameters.Graphical outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current problem.Results reveal that concentration upsurges as the micropolar parameter increases.The concentration reduces with an upsurge in the thermophoretic parameter.An upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass transport.The surface drag force declines for improved values of porosity and micropolar parameters.

Computer vision-aided DEM study on the compaction characteristics of graded subgrade filler considering realistic coarse particle shapes

The compaction quality of subgrade filler strongly affects subgrade settlement.The main objective of this research is to analyze the macro-and micro-mechanical compaction characteristics of subgrade filler based on the real shape of coarse particles.First,an improved Viola-Jones algorithm is employed to establish a digitalized 2D particle database for coarse particle shape evaluation and discrete modeling purposes of subgrade filler.Shape indexes of 2D subgrade filler are then computed and statistically analyzed.Finally,numerical simulations are performed to quantitatively investigate the effects of the aspect ratio(AR)and interparticle friction coefficient(μ)on the macro-and micro-mechanical compaction characteristics of subgrade filler based on the discrete element method(DEM).The results show that with the increasing AR,the coarse particles are narrower,leading to the increasing movement of fine particles during compaction,which indicates that it is difficult for slender coarse particles to inhibit the migration of fine particles.Moreover,the average displacement of particles is strongly influenced by the AR,indicating that their occlusion under power relies on particle shapes.The dis-placement and velocity of fine particles are much greater than those of the coarse particles,which shows that compaction is primarily a migration of fine particles.Under the cyclic load,the interparticle friction coefficientμhas little effect on the internal structure of the sample;under the quasi-static loads,however,the increase inμwill lead to a significant increase in the porosity of the sample.This study could not only provide a novel approach to investigate the compaction mechanism but also establish a new theoretical basis for the evaluation of intelligent subgrade compaction.

Research on the solid particle erosion wear of pipe steel for hydraulic fracturing based on experiments and numerical simulations

Erosion wear is a common failure mode in the oil and gas industry.In the hydraulic fracturing,the fracturing pipes are not only in high-pressure working environment,but also suffer from the impact of the high-speed solid particles in the fracturing fluid.Beneath such complex conditions,the vulnerable components of the pipe system are prone to perforation or even burst accidents,which has become one of the most serious risks at the fracturing site.Unfortunately,it is not yet fully understood the erosion mechanism of pipe steel for hydraulic fracturing.Therefore,this article provides a detailed analysis of the erosion behavior of fracturing pipes under complex working conditions based on experiments and numerical simulations.Firstly,we conducted erosion experiments on AISI 4135 steel for fracturing pipes to investigate the erosion characteristics of the material.The effects of impact angle,flow velocity and applied stress on erosion wear were comprehensively considered.Then a particle impact dynamic model of erosion wear was developed based on the experimental parameters,and the evolution process of particle erosion under different impact angles,impact velocities and applied stress was analyzed.By combining the erosion characteristics,the micro-structure of the eroded area,and the micro-mechanics of erosion damage,the erosion mechanism of pipe steel under fracturing conditions was studied in detail for the first time.Under high-pressure operating conditions,it was demonstrated through experiments and numerical simulations that the size of the micro-defects in the eroded area increased as the applied stress increased,resulting in more severe erosion wear of fracturing pipes.

Particle agglomeration and inhibition method in the fluidized pyrolysis reaction of waste resin

This work investigated the pyrolysis reaction of waste resin in a fluidized bed reactor.It was found that the pyrolysis-generated ash would adhere to the surface of ceramic particles,causing particle agglomeration and defluidization.Adding kaolin could effectively inhibit the particle agglomeration during the fluidized pyrolysis reaction through physical isolation and chemical reaction.On the one hand,kaolin could form a coating layer on the surface of ceramic particles to prevent the adhesion of organic ash generated by the pyrolysis of resin.On the other hand,when a sufficient amount of kaolin(-0.2%(mass))was added,the activated kaolin could fully contact with the Na+ ions generated by the pyrolysis of resin and react to form a high-melting aluminosilicate mineral(nepheline),which could reduce the formation of low-melting-point sodium sulfate and thereby avoid the agglomeration of ceramic particles.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

The research progress of an E//B neutral particle analyzer

An E//B neutral particle analyzer(NPA)has been designed and is under development at Sichuan University and Southwestern Institute of Physics.The main purpose of the E//B NPA is to measure the distribution function of fast ions in the HL-2A/3 tokamak.The E//B NPA contains three main units,i.e.the stripping unit,the analyzing unit and the detection unit.A gas stripping chamber was adopted as the stripping unit.The results of the simulations and beam tests for the stripping chamber are presented.Parallel electric and magnetic fields provided by a NdFeB permanent magnet and two parallel electric plates were designed and constructed for the analyzing unit.The calibration of the magnetic and electric fields was performed using a 50 kV electron cyclotron resonance ion source(ECRIS)platform.The detection unit consists of 32lutetium-yttrium oxyorthosilicate(LYSO)detector modules arranged in two rows.The response functions ofα,hydrogen ions(H^(+),H_(2)^(+)and H_(3)^(+))andγfor a detector module were measured with^(241)Am,^(137)Cs and^(152)Eu sources together with the 50 kV ECRIS platform.The overall results indicate that the designed E//B NPA device is capable of measuring the intensity of neutral hydrogen and deuteron atoms with energy higher than 20 keV.

Extended wet sieving method for determination of complete particle size distribution of general soils

The traditional standard wet sieving method uses steel sieves with aperture?0.063 mm and can only determine the particle size distribution(PSD)of gravel and sand in general soil.This paper extends the traditional method and presents an extended wet sieving method.The extended method uses both the steel sieves and the nylon filter cloth sieves.The apertures of the cloth sieves are smaller than 0.063 mm and equal 0.048 mm,0.038 mm,0.014 mm,0.012 mm,0.0063 mm,0.004 mm,0.003 mm,0.002 mm,and 0.001 mm,respectively.The extended method uses five steps to separate the general soil into many material sub-groups of gravel,sand,silt and clay with known particle size ranges.The complete PSD of the general soil is then calculated from the dry masses of the individual material sub-groups.The extended method is demonstrated with a general soil of completely decomposed granite(CDG)in Hong Kong,China.The silt and clay materials with different particle size ranges are further examined,checked and verified using stereomicroscopic observation,physical and chemical property tests.The results further confirm the correctness of the extended wet sieving method.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

A stable implicit nodal integration-based particle finite element method(N-PFEM)for modelling saturated soil dynamics

In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a generalised Hellinger-Reissner(HR)variational principle,creating an implicit PFEM formulation.To mitigate the volumetric locking issue in low-order elements,we employ a node-based strain smoothing technique.By discretising field variables at the centre of smoothing cells,we achieve nodal integration over cells,eliminating the need for sophisticated mapping operations after re-meshing in the PFEM.We express the discretised governing equations as a min-max optimisation problem,which is further reformulated as a standard second-order cone programming(SOCP)problem.Stresses,pore water pressure,and displacements are simultaneously determined using the advanced primal-dual interior point method.Consequently,our numerical model offers improved accuracy for stresses and pore water pressure compared to the displacement-based PFEM formulation.Numerical experiments demonstrate that the N-PFEM efficiently captures both transient and long-term hydro-mechanical behaviour of saturated soils with high accuracy,obviating the need for stabilisation or regularisation techniques commonly employed in other nodal integration-based PFEM approaches.This work holds significant implications for the development of robust and accurate numerical tools for studying saturated soil dynamics.

基于多源数据融合的半导体晶片CMP抛光材料去除率预测

目的 对半导体晶片抛光过程中的工艺参数、耗材使用量、抛光垫状态参数等多源数据预处理后进行数据融合,建立材料去除率(MRR)预测模型,为实现半导体晶片抛光加工工艺的决策和处理奠定基础。方法 研究晶片抛光加工中的数据特点及数据融合需求,提取数据集中每个晶片加工过程中的统计特征并生成新数据集,同时引入邻域特征以应对晶片加工过程中动态因素对材料去除率的影响。提出基于深度自动编码器的多源数据融合及材料去除率预测方法。设计深度自动编码器参数,优化深度自动编码器的损失函数从而增强深度自动编码器对强相关性特征变量的重建。基于深度自动编码器进行多源传感器信号融合,降低数据维度。使用超参数搜索算法优化BP神经网络超参数,利用BP神经网络方法将融合后的数据进行半导体晶片抛光过程中的材料去除率预测。结果 采用PHM2016数据集对模型进行验证,均方误差MSE达到7.862,相关性R^(2)达到91.2%。结论 基于多源数据的融合模型能有效预测MRR,可以对半导体晶片CMP工艺过程的智能决策与控制起到良好的辅助作用。

When river meets ocean: distribution and conversion of suspended organic particles in a Sundarbans mangrove river-estuary system, Bangladesh

Global carbon cycle has received extensive attention,among which the river-estuary system is one of the important links connecting the carbon cycle between land and ocean.In this paper,the distribution and control factors of particulate organic carbon(POC)were studied by using the data of organic carbon contents and its carbon isotopic composition(δ13C)in the mainstream and estuary of Passur River in the Sundarbans area,combined with the hydrological and biological data measured by CTD.The results show that POC content ranged from 0.263 mg/L to 9.292 mg/L,and the POC content in the river section(averaged 4.129 mg/L)was significantly higher than that in the estuary area(averaged 0.858 mg/L).Two distinct stages of POC transport from land to sea in the Sundarbans area were identified.The first stage occurred in the river section,where POC distribution was mainly controlled by the dynamic process of runoff and the organic carbon was mainly terrestrial source.The second stage occurred during estuarine mixing,where the POC distribution was mainly controlled by the mixing process of seawater and freshwater.The source of POC was predominantly marine and exhibiting vertical differences.The surface and middle layers were primarily influenced by marine sources,while the bottom layer was jointly controlled by terrestrial and marine sources of organic carbon.These findings are of great significance for understanding the carbon cycle in such a large mangrove ecosystem like the Sundarbans mangrove.

Study of deep transportation and plugging performance of deformable gel particles in porous media

Deformable gel particles(DGPs) possess the capability of deep profile control and flooding. However, the deep migration behavior and plugging mechanism along their path remain unclear. Breakage, an inevitable phenomenon during particle migration, significantly impacts the deep plugging effect. Due to the complexity of the process, few studies have been conducted on this subject. In this paper, we conducted DGP flow experiments using a physical model of a multi-point sandpack under various injection rates and particle sizes. Particle size and concentration tests were performed at each measurement point to investigate the transportation behavior of particles in the deep part of the reservoir. The residual resistance coefficient and concentration changes along the porous media were combined to analyze the plugging performance of DGPs. Furthermore, the particle breakage along their path was revealed by analyzing the changes in particle size along the way. A mathematical model of breakage and concentration changes along the path was established. The results showed that the passage after breakage is a significant migration behavior of particles in porous media. The particles were reduced to less than half of their initial size at the front of the porous media. Breakage is an essential reason for the continuous decreases in particle concentration, size, and residual resistance coefficient. However, the particles can remain in porous media after breakage and play a significant role in deep plugging. Higher injection rates or larger particle sizes resulted in faster breakage along the injection direction, higher degrees of breakage, and faster decreases in residual resistance coefficient along the path. These conditions also led to a weaker deep plugging ability. Smaller particles were more evenly retained along the path, but more particles flowed out of the porous media, resulting in a poor deep plugging effect. The particle size is a function of particle size before injection, transport distance, and different injection parameters(injection rate or the diameter ratio of DGP to throat). Likewise, the particle concentration is a function of initial concentration, transport distance, and different injection parameters. These models can be utilized to optimize particle injection parameters, thereby achieving the goal of fine-tuning oil displacement.

Mathematical modeling and simulations of stress mitigation by coating polycrystalline particles in lithium-ion batteries

A chemo-mechanical model is developed to investigate the effects on the stress development of the coating of polycrystalline Ni-rich LiNixMnyCo_(z)O_(2)(x≥0.8)(NMC)particles with poly(3,4-ethylenedioxythiophene)(PEDOT).The simulation results show that the coating of primary NMC particles significantly reduces the stress generation by efficiently accommodating the volume change associated with the lithium diffusion,and the coating layer plays roles both as a cushion against the volume change and a channel for the lithium transport,promoting the lithium distribution across the secondary particles more homogeneously.Besides,the lower stiffness,higher ionic conductivity,and larger thickness of the coating layer improve the stress mitigation.This paper provides a mathematical framework for calculating the chemo-mechanical responses of anisotropic electrode materials and fundamental insights into how the coating of NMC active particles mitigates stress levels.

GA-BP神经网络在抛光材料去除率预测中的应用

材料去除率(material removal rate,MRR)是抛光过程中调控工艺参数和评估加工效果的一项重要指标。为了有效地预测抛光过程中工件的材料去除率,建立了一种运用遗传算法(genetic algorithm,GA)与反向传播(back propagation,BP)神经网络相结合的MRR预测模型,利用GA的全局寻优能力对BP神经网络的初始权重和阈值进行优化。通过随机森林(random forest,RF)算法选择出对MRR预测结果影响显著的参数变量作为神经网络的输入,运用有关实验数据对网络模型进行训练与预测分析,并与同等条件下运用传统BP神经网络建立的预测模型进行对比分析。结果表明:GA-BP神经网络具有较高的预测精度。

Mass transfer enhancement and hydrodynamic performance with wire mesh coupling solid particles in bubble column reactor

It is of vital significance to investigate mass transfer enhancements for chemical engineering processes.This work focuses on investigating the coupling influence of embedding wire mesh and adding solid particles on bubble motion and gas-liquid mass transfer process in a bubble column.Particle image velocimetry(PIV)technology was employed to analyze the flow field and bubble motion behavior,and dynamic oxygen absorption technology was used to measure the gas-liquid volumetric mass transfer coefficient(kLa).The effect of embedding wire mesh,adding solid particles,and wire mesh coupling solid particles on the flow characteristic and kLa were analyzed and compared.The results show that the gas-liquid interface area increases by 33%-72%when using the wire mesh coupling solid particles strategy compared to the gas-liquid two-phase flow,which is superior to the other two strengthening methods.Compared with the system without reinforcement,kLa in the bubble column increased by 0.5-1.8 times with wire mesh coupling solid particles method,which is higher than the sum of kLa increases with inserting wire mesh and adding particles,and the coupling reinforcement mechanism for affecting gas-liquid mass transfer process was discussed to provide a new idea for enhancing gas-liquid mass transfer.

Optimizing laser coupling,matter heating,and particle acceleration from solids using multiplexed ultraintense lasers

Realizing the full potential of ultrahigh-intensity lasers for particle and radiation generation will require multi-beam arrangements due to technology limitations.Here,we investigate how to optimize their coupling with solid targets.Experimentally,we show that overlapping two intense lasers in a mirror-like configuration onto a solid with a large preplasma can greatly improve the generation of hot electrons at the target front and ion acceleration at the target backside.The underlying mechanisms are analyzed through multidimensional particle-in-cell simulations,revealing that the self-induced magnetic fields driven by the two laser beams at the target front are susceptible to reconnection,which is one possible mechanism to boost electron energization.In addition,the resistive magnetic field generated during the transport of the hot electrons in the target bulk tends to improve their collimation.Our simulations also indicate that such effects can be further enhanced by overlapping more than two laser beams.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Charging Properties and Particle Dynamics of Chang’e-5 Lunar Sample in an External Electric Field

Facing the challenges of in-situ utilization of lunar regolith resources,applying an external electric field to manipulate lunar particles has become a promising method for space particle control,which mainly depends on the particle charging properties in the applied electric field.Using the surficial lunar regolith samples brought back from the Moon by the Chang’e-5 mission(CE5 LS),this work successively studied their charging properties,particle dynamics,and their collision damages to aerospace materials under the action of an external electric field in high-vacuum conditions.The results indicated that the charging pro-cess and electrostatic projection of lunar regolith particles under high-vacuum conditions were different from those under atmosphere conditions.The particle diameter range of CE5 LS used in the experiment is 27.7-139.0 lm.For electric field strength of 3-12 kV·cm^(-1),the charge obtained by CE5 LS is 4.8×10^(-15)-4.7×10^(-13) C and the charge-to-mass ratio is 1.2×10^(-5)-6.8×10^(-4) C·kg^(-1).The CE5 LS is easier to be negatively charged in an external electric field.Furthermore,significant damages were observed on the target impact surfaces,indicating severe influences of lunar regolith particles on aerospace materials.Our work contributes to a more comprehensive understanding of physical mechanisms controlling the lunar regolith shielding and utilization,and will inspire broad efforts to develop the lunar in-situ engi-neering solutions.

Significance of including lid thickness and particle shape factor in numerical modeling for prediction of particle trap efficiency of invert trap

Sediment accumulation on the bed of open sewers and drains reduces hydraulic efficiency and can cause localized flooding.Slotted invert traps installed underneath the bed of open sewers and drains can eliminate sediment build-up by catching sediment load.Previous three-dimensional(3D)computational studies have examined the particle trapping performance of invert traps of different shapes and depths under varied sediment and flow conditions,considering particles as spheres.For two-dimensional and 3D numerical modeling,researchers assumed the lid geometry to be a thin line and a plane,respectively.In this 3D numerical study,the particle trapping efficiency of a slotted irregular hexagonal invert trap fitted at the flume bottom was examined by incorporating the particle shape factor of non-spherical sewage solid particles and the thicknesses of upstream and downstream lids over the trap in the discrete phase model of the ANSYS Fluent 2020 R1 software.The volume of fluid(VOF)and the realizable k-turbulence models were used to predict the velocity field.The two-dimensional particle image velocimetry(PIV)was used to measure the velocity field inside the invert trap.The results showed that the thicknesses of upstream and downstream lids affected the velocity field and turbulent kinetic energy at all flow depths.The joint impact of the particle shape factor and lid thickness on the trap efficiency was significant.When both the lid thickness and particle shape factor were considered in the numerical modeling,trap efficiencies were underestimated,with relative errors of-8.66%to-0.65%in comparison to the experimental values of Mohsin and Kaushal(2017).They were also lower than the values predicted by Mohsin and Kaushal(2017),which showed an overall overestimation with errors of-2.3%to 17.4%.