Monte Carlo study on abnormal growth of Goss grains in Fe-3%Si steel induced by second-phase particles

The selective abnormal growth of Goss grains in magnetic sheets of Fe-3%Si （grade Hi-B） induced by second-phase particles （AlN and MnS） was studied using a modified Monte Carlo Ports model. The starting microstructures for the simulations were generated from electron backscatter diffraction （EBSD） orientation imaging maps of recrystallized samples. In the simulation, second-phase particles were assumed to be randomly distributed in the initial microstructures and the Zener drag effect of particles on Goss grain boundaries was assumed to be selectively invalid because of the unique properties of Goss grain boundaries. The simulation results suggest that normal growth of the matrix grains stagnates because of the pinning effect of particles on their boundaries. During the onset of abnormal grain growth, some Goss grains with concave boundaries in the initial microstructure grow fast abnormally and other Goss grains with convex boundaries shrink and eventually disappear.

Effect of second-phase particle size and presence of vibration on AZ91/SiC surface composite layer produced by FSP

An improved method of friction stir processing(FSP)was introduced for the processing of AZ91 magnesium alloy specimens.This novel process was called“friction stir vibration processing(FSVP)”.FSP and FSVP were utilized to develop surface composites on the studied alloy while SiC nanoparticles were applied as second-phase particles.The effect of reinforcing SiC particles with different sizes(30 and 300 nm)on different characteristics of the composite surface was studied.The results indicated that the microstructure was refined and mechanical properties such as hardness,ductility,and strength were enhanced as FSVP was applied.Furthermore,it was concluded that the effect of reinforcing particles with a size of 30 nm on the microstructure and mechanical properties of the surface composite was more obvious than that of particles with a size of 300 nm.It was also found that mechanical properties and microstructure of FSV-processed specimens were improved as vibration frequency increased.The hardness value in the stir zone was about 157 MPa for the FSV-processed specimen at a vibration frequency of 50 Hz,while this value was around 116 MPa for the FSV-processed specimen at a vibration frequency of 25 Hz.

EFFECTS OF DISPERSED SECOND-PHASE PARTICLES ON MIXED CONDUCTORS OF Li_(1+x)V_3O_8(X=0, 1)

The structure and electric properties of the mixed conductors of Li_(1+x)V_3O_8(x=0.1) containing dispersed second-phase α-Al_2O_3 particles have been studied in the paper. The total conductivity of the specimen containing 6m/o α-Al_2O_3 is five or six times as much as that of pure Li_(1+x)V_3O_8 (x=0.1) at room temperature. By the analyses of NMR and ESR, it is known that at low temperature, the transport activation energy of Li^+ ions reduced, the transport frequency of Li^+ ions are speeded up and thus ionic conductivity increased in the specimens containing α-Al_2O_3 particles. At high temperature, diffusion effect of α-Al_2O_3 on electronic transport becomes great, the enhancement of ionic conductivity caused by α-Al_2O_3 doesn't make up the decrease of electronic conductivity caused by the diffusion effect, so that total conductivity of the specimens containing DSPP is lower than that of pure Li_(1+x)V_3O_8(x=0.1). As a result, DSPP mainly increases ionic conductivity of the mixed conductors at room temperature.

Strengthening and control of second-phase particle precipitation in ferritic/austenitic/martensitic heat-resistant alloys:a review

Heat-resistant alloys with excellent mechanical properties are widely used in various fields,and further improvement in their properties is essential to meet the requirements in new-generation advanced supercritical boilers,nuclear reactors,superheaters,and other new materials applications.To effectively enhance the comprehensive performance of heat-resistant alloys,second-phase particle strengthening has been widely studied,and in the face of different service envi-ronments of advanced heat-resistant steels,the selection of suitable second-phase particles is essential to maximize the performance of these alloys.To this end,three major types of reinforcing phases in heat-resistant alloys such as carbides,rare earth oxides,and intermetallic compounds are summarized.A comparative analysis of the precipitation behavior of the reinforcing phases with different types as well as the risks and means of controlling their use in service,is presented.Key parameters for the application of various types of second-phase particles in heat-resistant alloys are provided to support the design and preparation of new ultrahigh-performance heat-resistant alloys.

Influence of undissolved second-phase particles on dynamic recrystallization behavior of Mg–7Sn–1Al–1Zn alloy during low-and high-temperature extrusions

This study investigates the effects of fine and coarse undissolved particles in a billet of the Mg–7 Sn–1 Al–1 Zn(TAZ711)alloy on the dynamic recrystallization(DRX)behavior during hot extrusion at low and high temperatures and the resultant microstructure and mechanical properties of the alloy.To this end,partially homogenized(PH)and fully homogenized(FH)billets are extruded at temperatures of250 and 450°C.The PH billet contains fine and coarse undissolved Mg_(2) Sn particles in the interdendritic region and along the grain boundaries,respectively.The fine particles(<1μm in size)retard DRX during extrusion at 250°C via the Zener pinning effect,and this retardation causes a decrease in the area fraction of dynamically recrystallized(DRXed)grains of the extruded alloy.In addition,the inhomogeneous distribution of fine particles in the PH billet leads to the formation of a bimodal DRXed grain structure with excessively grown grains in particle-scarce regions.In contrast,in the FH billet,numerous nanosized Mg_(2) Sn precipitates are formed throughout the material during extrusion at 250°C,which,in turn,leads to the formation of small,uniform DRXed grains by the grain-boundary pinning effect of the precipitates.When the PH billet is extruded at the high temperature of 450°C,the retardation effect of the fine particles on DRX is weakened by their dissolution in theα-Mg matrix and the increased extent of thermally activated grain-boundary migration.In contrast,the coarse Mg_(2) Sn particles in the billet promote DRX during extrusion through the particle-stimulated nucleation phenomenon,which results in an increase in the area fraction of DRXed grains.At both low and high extrusion temperatures,the extruded material fabricated using the PH billet,which contains both fine and coarse undissolved particles,has a lower tensile strength than that fabricated using the FH billet,which is virtually devoid of second-phase particles.This lower strength of the former is attributed mainly to the larger grains and/or absence of nanosized M2 Sn precipitates in it.

Microstructural characteristics and second-phase particles in yttrium-bearing Fe-10Ni-7Mn martensitic steels

In this study, the microstructure and second-phase particles in yttrium （0.05 wt.%and 0.8 wt.%） bearing Fe-10Ni-7Mn steels were characterized. The results of X-ray analysis as well as scanning electron microscopy coupled with energy dispersive X-ray spectroscopy indicated the formation of （Fe, Ni, Mn）17Y2 precipitates with hexagonal structure in a Fe-10Ni-7Mn-0.8Y （wt.%） alloy. Lattice parameters of these precipitates were calculated as follows：a=0.8485 nm and c=0.8274 nm. Formation of Y2O3 sub-micron particles was also confirmed in both yttrium bearing steels via electrolytic phase extraction method. The effect of these precipitates on the prior austenite grain size was investigated. The results revealed that these precipitates had an effective role in controlling the prior austenite grain size.

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.

A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.

Improving the Position Accuracy and Computational Efficiency of UAV Terrain Aided Navigation Using a Two-Stage Hybrid Fuzzy Particle Filtering Method

Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.

Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

Improvement of warm formability of Al-Mg sheet alloys containing coarse second-phase particles

Several alloying elements involving Zr, Cu, Zn and Sc were added to Al-Mg sheet alloys in order to obtain an excellent combination of high strength and good high-temperature formability. Microstruc-tural examination showed that coarse intermetallic particles were formed in the microstructure and their amounts changed with variations of the alloying elements. During warm rolling of thermome-chanical treatments prior to warm deformation, the coarse particles initiated cracks, decreasing the warm formability. For healing the crack damage and further improving the warm formability, a process of hot isothermal press was developed and optimized to the sheet alloys. With this process, the biaxial stretch formability at 350℃ was improved by 22% for an aluminum alloy containing a large amount of coarse particles.

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.

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.