This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in ...This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.展开更多
Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haz...Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haze,remains very scarce.This study used single-particle aerosol mass spectrometry to investigate IM-containing particles in Chengdu,one of the megacities in the SCB,during summer and winter before and after implemen-tation of the Three-year Action Plan to Win the Blue-Sky Defense War(BSDW).We found that IM-containing particles accounted for 1.2%–12.0%of all detected particles,and they highly mixed with carbonaceous com-ponents,secondary inorganic species,and organic nitrogen.From before to after the BSDW,the proportion of IM-containing particles decreased by 1.8%in summer,but increased by 9.6%in winter.Ammonium/amines and carbonyl compounds were closely related to IM-containing particles;the highest proportion of IM-containing particles occurred in particles mixed with amines and carbonyls.The number fraction of IM-containing particles in all seasons was higher at night than during daytime.The potential source areas of IM-containing particles showed notable narrowing after the BSDW,and the high-value areas were found distributed closer to Chengdu and its surrounding areas.In the winter before the BSDW,most IM-containing particles(>70%)were mixed with organic carbon(OC)particles,and the contributions of OC and mixed organic–elemental carbon(OC-EC)particles increased with aggravation of pollution,whereas OC-EC and Metal particles played a more crucial role in the winter after the BSDW.展开更多
We present a numerical framework for simulating viscous compressible flows in the presence of solid particles with large size ratios.The volume-filtered Navier-Stokes equations are discretized using a class of high-or...We present a numerical framework for simulating viscous compressible flows in the presence of solid particles with large size ratios.The volume-filtered Navier-Stokes equations are discretized using a class of high-order low-dissipative finite difference operators with energy-preserving properties.No-slip,adiabatic boundary conditions are enforced at the surface of large particles(with diameters significantly larger than the local grid spacing)using a ghost-point immersed boundary method.Two-way coupling between the gas phase and small particles(with diameters proportional to the grid spacing)is accounted for through volumetric source terms for interphase momentum and energy exchange.A simple and efficient approach for collision detection between small and large particles is proposed.The framework is applied to simulations of planar shocks interacting with bidisperse distributions of particles with size ratios of approximately thirty.Particle dispersion and size segregation are reported and a simple analytical model for size segregation is proposed.展开更多
This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared ...This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared spectroscopy,X-ray diffraction,high-performance anion-exchange chromatography,and differential scanning calorimetry.Significant differences were observed in their morphological,physicochemical,and functional properties.PMS had a smaller particle size(13.68 μm),irregular polygonal shape,A-type,lower water absorption(62.67 %),and higher oil absorption(51.17 %).In contrast,SGS exhibited larger particles(31.75 μm),a nearly spherical shape,B-type,higher crystallinity(50.66 %),and greater amylose content(21.54 %),with superior thermal stability,shear resistance,and gelatinization enthalpy.SGS also contained higher resistant starch(83.28 %) and longer average chain length(20.58 %),but showed lower solubility,swelling power,light transmittance,and freeze-thaw stability.The physicochemical properties differences in crystal pattern and particle morphology between PMS and SGS lead to distinct behaviors during in vitro digestion and fermentation.These findings highlight the potential of medicinal plant starches in functional ingredients and industrial processes.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Bacterial cells are widely accepted as nucleation sites for calcium carbonate precipitation in biomineralization based on the Microbially Induced Carbonate Precipitation(MICP)process.For MICP-based insitu biotreatment...Bacterial cells are widely accepted as nucleation sites for calcium carbonate precipitation in biomineralization based on the Microbially Induced Carbonate Precipitation(MICP)process.For MICP-based insitu biotreatment,the firstproblem to be solved is how to introduce and retain the bacterial cells in the soil,which involves the migration and retention of bacterial cells during the biogrouting process.Soil particle size,a key factor in determining pore throat size,can have a significanteffect on the migration and retention of bacterial cells in the soil and therefore on biomineralization.To investigate the effect of particle size on the migration and retention of bacterial cells in sand and its biomineralization,two sets of tests were carried out in this study,including percolation tests and sand column treatment tests.Soil urease activity(definedas urease activity per unit mass of soil)and calcium carbonate content of the biomineralized sand were measured to comprehensively assess the migration and retention of bacterial cells in the sand.The results indicate that sands with a particle size smaller than 0.25 mmwould inhibit the migration of bacteria in the sand,resulting in a nonuniform distribution of precipitated calcium carbonate and a low strength enhancement of biomineralization.On the other hand,sands with a particle size larger than 1.18 mm are unfavorable for retaining bacterial cells in the sand,resulting in low calcium conversion efficiency.Meanwhile,particle size would also affect the formation of effective calcium carbonate through interparticle contact number and interparticle pore size,and thus biomineralization.展开更多
Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study invest...Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study investigates their coupled effects using inclinedflume experiments and Discrete Element Method(DEM)simulations,covering three gravel sizes(2-5 mm,5-7 mm,7-10 mm)and four ice-content levels(0%,20%,40%,60%).Run-out distance,velocity,energy components,flow regime(Savage number),and segregation indexαwere quantified.Increasing ice content significantly enhances mobility,but with diminishing marginal effectiveness.From 0%to 40%ice content,run-out distance increases by 41%-86%,whereas the additional increase from 40%to 60%contributes only 12%-23%.Particle-size ratio strongly governs segregation intensity.Fine-gravel groups reach segregation indices ofα=0.92-0.98,indicating nearly complete upward migration of ice,whereas medium-gravel and coarse-gravel groups exhibit much weaker segregation,stabilizing atα=0.68-0.74 and 0.60-0.69.Savage number analyses reveal marked flow-regime transitions.At 0%ice content,Savage numbers reach 1.0-1.5,indicating a collisional regime.Increasing ice content suppresses collisionality,with Savage numbers decreasing to 0.03-0.07 at 60%ice content,consistent with dense-regime flow.DEM energy analyses confirm this regime shift:for finegravel mixtures,collision energy decreases by 14%,while sliding-friction energy increases by 33%as ice content increases from 0%to 60%,reflecting enhanced overburden effects imposed by upward-segregated ice layers.Medium and coarse mixtures exhibit weaker or opposite energy-shift patterns,demonstrating strong size dependence.Mechanistically,large particle-size contrasts promote strong segregation and form dense basal rock layers that increase basal friction and reduce mobility.When particle sizes are similar or ice content is high,segregation remains limited,allowing ice to mix into the basal layer,thereby reducing basal friction and enhancing mobility.This research quantitatively demonstrates how composition controls particle spatial distribution,flow regime,and energy dissipation,offering new mechanistic insights into the propagation and deposition behaviors of rock-ice avalanches and improving hazard assessment in vulnerable high-mountain regions.展开更多
The stress-strain behavior of calcareous sand is significantly influencedby particle breakage(B)and initial relative density(Dri),but few constitutive models consider their combined effects.To bridge this gap,we condu...The stress-strain behavior of calcareous sand is significantly influencedby particle breakage(B)and initial relative density(Dri),but few constitutive models consider their combined effects.To bridge this gap,we conducted a series of triaxial tests on calcareous sand with varying Dri and stress paths,examining particle breakage and critical state behavior.Key findingsinclude:(1)At a constant stress ratio(η),B follows a hyperbolic relationship with mean effective stress(p'),and for a given p',B increases proportionally withη;(2)The critical state line(CSL)moves downward with increasing Dri,whereas the critical state friction angle(φcs)decreases with increasing B.Based on these findings,we propose a unifiedbreakage evolution model to quantify particle breakage in calcareous sand under various loading conditions.Integrating this model with the Normal Consolidation Line(NCL)and CSL equations,we successfully simulate the steepening of NCL and CSL slopes as B increases with the onset of particle breakage.Furthermore,we quantitatively evaluate the effect of B onφcs.Finally,within the framework of Critical State Soil Mechanics and Hypoplasticity theory,we develop a hypoplastic model incorporating B and Dri.The model is validated through strong agreement with experimental results across various initial relative densities,stress paths and drainage conditions.展开更多
In this study,we perform particle-resolved simulations of settling spheroidal particles,considering oblate and prolate spheroids and spheres,and investigate the shape effect on the particle dynamics in suspensions wit...In this study,we perform particle-resolved simulations of settling spheroidal particles,considering oblate and prolate spheroids and spheres,and investigate the shape effect on the particle dynamics in suspensions with volume fraction 1%and 5%.We first examine the single-point statistics of the translational and rotational motion of the settling particles.The horizontal velocity has a symmetrical distribution with standard deviation dependent on the particle shape.The greater horizontal velocity fluctuations of the non-spherical particles,compared to that of spheres,are attributed to the horizontal drift of settling spheroids with oblique orientations induced by the fluid-particle and particle-particle interactions.The fluctuation of particle vertical velocity,instead,is skewed under the effect of wake-induced hydrodynamic interactions.Further,we explore the particle pair statistics,which demonstrate the formation of column-like particle micro-structures for the lowest volume fraction considered.This clustering is more pronounced for spheroidal particles than spheres,due to the stronger attractions among vertically-aligned settling spheroids.Moreover,the particle pair statistics are directly related to the collision rate among the dispersed particles.The local accumulation of oblate/prolate spheroids serves as the major mechanism to promote the particle-particle collisions in dilute suspensions.展开更多
Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Par...Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.展开更多
Particle-and droplet-laden flows are central to many problems in mechanics and transport.They occur in sedimentladen boundary layers,gas-solid and gas-liquid dispersions,and surface-water films driven by external forc...Particle-and droplet-laden flows are central to many problems in mechanics and transport.They occur in sedimentladen boundary layers,gas-solid and gas-liquid dispersions,and surface-water films driven by external forcing.They also underpin practical applications ranging from environmental transport to high-speed and aerothermal systems.Despite decades of progress,prediction remains difficult.The physics spans a wide range of scales and often couples turbulence,interphase momentum exchange,collisions,and interfacial transport.Reliable computation therefore requires both robust numerical methodology and careful physical interpretation.展开更多
An in-depth understanding of the behaviours of solid propellants under low-velocity impact loads is crucial for enhancing their safety in applications such as aerospace propulsion.This study investigated the dynamic r...An in-depth understanding of the behaviours of solid propellants under low-velocity impact loads is crucial for enhancing their safety in applications such as aerospace propulsion.This study investigated the dynamic responses of single ammonium perchlorate(AP)/octogen(HMX)particles embedded in a hydroxyl-terminated polybutadiene(HTPB)binder under dynamic compression loading via real-time synchrotron-based X-ray phase contrast imaging and a modified split Hopkinson pressure bar(SHPB)system.The compression of the viscoelastic binder and subsequent dynamic fracturing of the AP/HMX particles were captured.During compression,transverse cracks developed within the AP particles,and their propagation led to particle fracturing,resulting in ductile fracturing.Unlike AP,HMX generated numerous short cracks within the internal and edge regions simultaneously,leading to fragmentation and brittle fracturing.Moreover,particle damage reduced the modulus of the sample,shifting its dynamic stress response from nonlinear elasticity to strain softening and further strain hardening as the binder exhibited plastic deformation.A compression simulation incorporating a real particle microscopic structure was established to study the mechanical response of the interface and particles.The simulation results agreed with the experimental observations.These results indicate that the shear stress at the HTPB-AP interface is greater than that at the HTPB-HMX interface,which is a factor influencing the differences in the mesoscale damage mechanisms of the particles.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this...During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.展开更多
Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces th...Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization.展开更多
Ice lens initiation is the core issue in understanding the dynamic process of frost heave.However,there are still limitations to find an adequate criterion for describing the formation of ice lens.A series of one-dime...Ice lens initiation is the core issue in understanding the dynamic process of frost heave.However,there are still limitations to find an adequate criterion for describing the formation of ice lens.A series of one-dimensional freezing tests is designed using the particle image velocimetry(PIV)method to monitor the frost heave and ice lens formation.The results show that the conventional parameters,such as displacement and velocity,cannot be used to track the ice lens formation,while the strain can be employed to detect the ice lens formation and investigate the freezing change patterns.This study proposes strain as a new criterion for assessing ice lens initiation,applicable across various soil types and freezing conditions(constant freezing and ramped freezing).The strain change in the region where the ice lens forms is the largest during the freezing process.Additionally,strain curves at the top of the soil samples can reveal different freezing patterns and distinguish the first and second frost heave stages.This newly developed technology enables continuous,non-destructive monitoring of ice lens initiation across diverse conditions and soil types,enhancing data visualization and three-dimensional modeling of freezing parameters while improving traditional methods by directly measuring velocity and strain in frost heave investigations.The study is expected to enhance the research of ice lens criterion and provide a new perspective for monitoring the freezing process.展开更多
Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiment...Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.展开更多
In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the...In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability.展开更多
This study investigates the motion behavior of a slender flexible particle in a backward-facing step(BFS)flow using the direct-forcing fictitious domain method,with a particular focus on the trapping phenomena near th...This study investigates the motion behavior of a slender flexible particle in a backward-facing step(BFS)flow using the direct-forcing fictitious domain method,with a particular focus on the trapping phenomena near the separation vortex region.Three distinct motion modes are identified:periodic rotation or oscillation within the vortex(trapping),downstream transport(escape),and transition state exhibiting unstable trapping.A dynamic balance among inward migration,centrifugal effects,wall interactions,and elastic forces enables the particle to achieve stable orbital motion within two distinct limit cycles.The topology of these orbits is governed by parameters,including the aspect ratio,structural flexibility,deformation intensity,and fluid inertia,all of which are characterized by the Reynolds number(Re).Specifically,fluid inertia plays a dominant role in facilitating particle trapping.At a fixed Re,a particle with a smaller aspect ratio tends to migrate inward and become trapped,whereas one with a larger aspect ratio is more likely to escape.Structural flexibility,especially when enhanced by confinement near the wall,leads to elastic deformation that induces secondary vortices and a weak flipping motion.The deformation intensityαsignificantly influences the lateral migration of the slender particle after the initial release;a largerαcauses it to drift toward the channel centerline,increasing the probability of escape.These findings provide a theoretical foundation for optimizing the transport and capture of slender soft swimmers in complex flow environments.展开更多
基金supported by the P.G.Senapathy Center for Computing Resources at IIT Madrasfunding provided by the Ministry of Education,Government of Indiasupported by the National Natural Science Foundation of China(Grant Nos.12388101,12472224 and 92252104).
文摘This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.
基金supported by Sichuan Science and Technology Program(No.2024NSFSC0060)the National Natural Science Foundation of China(No.U23A2030)the Basic Research Cultivation Support Plan of Southwest Jiaotong University(No.2682023ZTPY016).
文摘Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haze,remains very scarce.This study used single-particle aerosol mass spectrometry to investigate IM-containing particles in Chengdu,one of the megacities in the SCB,during summer and winter before and after implemen-tation of the Three-year Action Plan to Win the Blue-Sky Defense War(BSDW).We found that IM-containing particles accounted for 1.2%–12.0%of all detected particles,and they highly mixed with carbonaceous com-ponents,secondary inorganic species,and organic nitrogen.From before to after the BSDW,the proportion of IM-containing particles decreased by 1.8%in summer,but increased by 9.6%in winter.Ammonium/amines and carbonyl compounds were closely related to IM-containing particles;the highest proportion of IM-containing particles occurred in particles mixed with amines and carbonyls.The number fraction of IM-containing particles in all seasons was higher at night than during daytime.The potential source areas of IM-containing particles showed notable narrowing after the BSDW,and the high-value areas were found distributed closer to Chengdu and its surrounding areas.In the winter before the BSDW,most IM-containing particles(>70%)were mixed with organic carbon(OC)particles,and the contributions of OC and mixed organic–elemental carbon(OC-EC)particles increased with aggravation of pollution,whereas OC-EC and Metal particles played a more crucial role in the winter after the BSDW.
基金This work used Expanse systems at UCSD through an allocation[PHY240089]from the Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support(ACCESS)programsupported by U.S.National Science Foundation(Grant Nos.2138259,2138286,2138307,2137603 and 2138296).
文摘We present a numerical framework for simulating viscous compressible flows in the presence of solid particles with large size ratios.The volume-filtered Navier-Stokes equations are discretized using a class of high-order low-dissipative finite difference operators with energy-preserving properties.No-slip,adiabatic boundary conditions are enforced at the surface of large particles(with diameters significantly larger than the local grid spacing)using a ghost-point immersed boundary method.Two-way coupling between the gas phase and small particles(with diameters proportional to the grid spacing)is accounted for through volumetric source terms for interphase momentum and energy exchange.A simple and efficient approach for collision detection between small and large particles is proposed.The framework is applied to simulations of planar shocks interacting with bidisperse distributions of particles with size ratios of approximately thirty.Particle dispersion and size segregation are reported and a simple analytical model for size segregation is proposed.
基金supported by the National Natural Science Foundation of China (No.82174074)。
文摘This study investigates the properties of high-purity starches extracted from Polygonum multiflorum(PMS)and Smilax glabra(SGS).The starches were characterized by scanning electron microscopy,Fouriertransform infrared spectroscopy,X-ray diffraction,high-performance anion-exchange chromatography,and differential scanning calorimetry.Significant differences were observed in their morphological,physicochemical,and functional properties.PMS had a smaller particle size(13.68 μm),irregular polygonal shape,A-type,lower water absorption(62.67 %),and higher oil absorption(51.17 %).In contrast,SGS exhibited larger particles(31.75 μm),a nearly spherical shape,B-type,higher crystallinity(50.66 %),and greater amylose content(21.54 %),with superior thermal stability,shear resistance,and gelatinization enthalpy.SGS also contained higher resistant starch(83.28 %) and longer average chain length(20.58 %),but showed lower solubility,swelling power,light transmittance,and freeze-thaw stability.The physicochemical properties differences in crystal pattern and particle morphology between PMS and SGS lead to distinct behaviors during in vitro digestion and fermentation.These findings highlight the potential of medicinal plant starches in functional ingredients and industrial processes.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金support by the National Natural Science Foundation of China(NSFC)(Grant Nos.52178319,42477160,52338007).
文摘Bacterial cells are widely accepted as nucleation sites for calcium carbonate precipitation in biomineralization based on the Microbially Induced Carbonate Precipitation(MICP)process.For MICP-based insitu biotreatment,the firstproblem to be solved is how to introduce and retain the bacterial cells in the soil,which involves the migration and retention of bacterial cells during the biogrouting process.Soil particle size,a key factor in determining pore throat size,can have a significanteffect on the migration and retention of bacterial cells in the soil and therefore on biomineralization.To investigate the effect of particle size on the migration and retention of bacterial cells in sand and its biomineralization,two sets of tests were carried out in this study,including percolation tests and sand column treatment tests.Soil urease activity(definedas urease activity per unit mass of soil)and calcium carbonate content of the biomineralized sand were measured to comprehensively assess the migration and retention of bacterial cells in the sand.The results indicate that sands with a particle size smaller than 0.25 mmwould inhibit the migration of bacteria in the sand,resulting in a nonuniform distribution of precipitated calcium carbonate and a low strength enhancement of biomineralization.On the other hand,sands with a particle size larger than 1.18 mm are unfavorable for retaining bacterial cells in the sand,resulting in low calcium conversion efficiency.Meanwhile,particle size would also affect the formation of effective calcium carbonate through interparticle contact number and interparticle pore size,and thus biomineralization.
基金funded by the Natural Science Foundation of China(Grants No 42277127)。
文摘Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study investigates their coupled effects using inclinedflume experiments and Discrete Element Method(DEM)simulations,covering three gravel sizes(2-5 mm,5-7 mm,7-10 mm)and four ice-content levels(0%,20%,40%,60%).Run-out distance,velocity,energy components,flow regime(Savage number),and segregation indexαwere quantified.Increasing ice content significantly enhances mobility,but with diminishing marginal effectiveness.From 0%to 40%ice content,run-out distance increases by 41%-86%,whereas the additional increase from 40%to 60%contributes only 12%-23%.Particle-size ratio strongly governs segregation intensity.Fine-gravel groups reach segregation indices ofα=0.92-0.98,indicating nearly complete upward migration of ice,whereas medium-gravel and coarse-gravel groups exhibit much weaker segregation,stabilizing atα=0.68-0.74 and 0.60-0.69.Savage number analyses reveal marked flow-regime transitions.At 0%ice content,Savage numbers reach 1.0-1.5,indicating a collisional regime.Increasing ice content suppresses collisionality,with Savage numbers decreasing to 0.03-0.07 at 60%ice content,consistent with dense-regime flow.DEM energy analyses confirm this regime shift:for finegravel mixtures,collision energy decreases by 14%,while sliding-friction energy increases by 33%as ice content increases from 0%to 60%,reflecting enhanced overburden effects imposed by upward-segregated ice layers.Medium and coarse mixtures exhibit weaker or opposite energy-shift patterns,demonstrating strong size dependence.Mechanistically,large particle-size contrasts promote strong segregation and form dense basal rock layers that increase basal friction and reduce mobility.When particle sizes are similar or ice content is high,segregation remains limited,allowing ice to mix into the basal layer,thereby reducing basal friction and enhancing mobility.This research quantitatively demonstrates how composition controls particle spatial distribution,flow regime,and energy dissipation,offering new mechanistic insights into the propagation and deposition behaviors of rock-ice avalanches and improving hazard assessment in vulnerable high-mountain regions.
基金support to this study from the National Natural Science Foundation of China,NSFC(Grant No.52278367)The Belt and Road Special Foundation of the National Key Laboratory ofWater Disaster Prevention(Grant No.2024nkms08).
文摘The stress-strain behavior of calcareous sand is significantly influencedby particle breakage(B)and initial relative density(Dri),but few constitutive models consider their combined effects.To bridge this gap,we conducted a series of triaxial tests on calcareous sand with varying Dri and stress paths,examining particle breakage and critical state behavior.Key findingsinclude:(1)At a constant stress ratio(η),B follows a hyperbolic relationship with mean effective stress(p'),and for a given p',B increases proportionally withη;(2)The critical state line(CSL)moves downward with increasing Dri,whereas the critical state friction angle(φcs)decreases with increasing B.Based on these findings,we propose a unifiedbreakage evolution model to quantify particle breakage in calcareous sand under various loading conditions.Integrating this model with the Normal Consolidation Line(NCL)and CSL equations,we successfully simulate the steepening of NCL and CSL slopes as B increases with the onset of particle breakage.Furthermore,we quantitatively evaluate the effect of B onφcs.Finally,within the framework of Critical State Soil Mechanics and Hypoplasticity theory,we develop a hypoplastic model incorporating B and Dri.The model is validated through strong agreement with experimental results across various initial relative densities,stress paths and drainage conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.92252104,12388101,and 12472224).
文摘In this study,we perform particle-resolved simulations of settling spheroidal particles,considering oblate and prolate spheroids and spheres,and investigate the shape effect on the particle dynamics in suspensions with volume fraction 1%and 5%.We first examine the single-point statistics of the translational and rotational motion of the settling particles.The horizontal velocity has a symmetrical distribution with standard deviation dependent on the particle shape.The greater horizontal velocity fluctuations of the non-spherical particles,compared to that of spheres,are attributed to the horizontal drift of settling spheroids with oblique orientations induced by the fluid-particle and particle-particle interactions.The fluctuation of particle vertical velocity,instead,is skewed under the effect of wake-induced hydrodynamic interactions.Further,we explore the particle pair statistics,which demonstrate the formation of column-like particle micro-structures for the lowest volume fraction considered.This clustering is more pronounced for spheroidal particles than spheres,due to the stronger attractions among vertically-aligned settling spheroids.Moreover,the particle pair statistics are directly related to the collision rate among the dispersed particles.The local accumulation of oblate/prolate spheroids serves as the major mechanism to promote the particle-particle collisions in dilute suspensions.
基金supported by National Natural Science Foundation of China(62106092)Natural Science Foundation of Fujian Province(2024J01822,2024J01820,2022J01916)Natural Science Foundation of Zhangzhou City(ZZ2024J28).
文摘Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.
文摘Particle-and droplet-laden flows are central to many problems in mechanics and transport.They occur in sedimentladen boundary layers,gas-solid and gas-liquid dispersions,and surface-water films driven by external forcing.They also underpin practical applications ranging from environmental transport to high-speed and aerothermal systems.Despite decades of progress,prediction remains difficult.The physics spans a wide range of scales and often couples turbulence,interphase momentum exchange,collisions,and interfacial transport.Reliable computation therefore requires both robust numerical methodology and careful physical interpretation.
基金supported by the National Natural Science Foundation of China(U2341288 and 12302492)。
文摘An in-depth understanding of the behaviours of solid propellants under low-velocity impact loads is crucial for enhancing their safety in applications such as aerospace propulsion.This study investigated the dynamic responses of single ammonium perchlorate(AP)/octogen(HMX)particles embedded in a hydroxyl-terminated polybutadiene(HTPB)binder under dynamic compression loading via real-time synchrotron-based X-ray phase contrast imaging and a modified split Hopkinson pressure bar(SHPB)system.The compression of the viscoelastic binder and subsequent dynamic fracturing of the AP/HMX particles were captured.During compression,transverse cracks developed within the AP particles,and their propagation led to particle fracturing,resulting in ductile fracturing.Unlike AP,HMX generated numerous short cracks within the internal and edge regions simultaneously,leading to fragmentation and brittle fracturing.Moreover,particle damage reduced the modulus of the sample,shifting its dynamic stress response from nonlinear elasticity to strain softening and further strain hardening as the binder exhibited plastic deformation.A compression simulation incorporating a real particle microscopic structure was established to study the mechanical response of the interface and particles.The simulation results agreed with the experimental observations.These results indicate that the shear stress at the HTPB-AP interface is greater than that at the HTPB-HMX interface,which is a factor influencing the differences in the mesoscale damage mechanisms of the particles.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by the National Natural Science Foundation of China(No.52175414)the Natural Science Foundation of Jiangsu Province of China(No.BK20220134)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.NE2023002)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(No.KYCX24_0559)。
文摘During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.
基金Project supported by the Project of the Anhui Provincial Natural Science Foundation(Grant No.2308085MA19)Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA0410401)+2 种基金the National Natural Science Foundation of China(Grant No.52202120)the National Key Research and Development Program of China(Grant No.2023YFA1609800)USTC Research Funds of the Double First-Class Initiative(Grant No.YD2310002013)。
文摘Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization.
基金supported by the National Natural Science Foundation of China(Grant No.52178376)National Key R&D Program of China(Grant No.2022YFB2603301)Science and Technology Research and Development Program of China Railway Group Limited(Grant No.2022-ZD-13).
文摘Ice lens initiation is the core issue in understanding the dynamic process of frost heave.However,there are still limitations to find an adequate criterion for describing the formation of ice lens.A series of one-dimensional freezing tests is designed using the particle image velocimetry(PIV)method to monitor the frost heave and ice lens formation.The results show that the conventional parameters,such as displacement and velocity,cannot be used to track the ice lens formation,while the strain can be employed to detect the ice lens formation and investigate the freezing change patterns.This study proposes strain as a new criterion for assessing ice lens initiation,applicable across various soil types and freezing conditions(constant freezing and ramped freezing).The strain change in the region where the ice lens forms is the largest during the freezing process.Additionally,strain curves at the top of the soil samples can reveal different freezing patterns and distinguish the first and second frost heave stages.This newly developed technology enables continuous,non-destructive monitoring of ice lens initiation across diverse conditions and soil types,enhancing data visualization and three-dimensional modeling of freezing parameters while improving traditional methods by directly measuring velocity and strain in frost heave investigations.The study is expected to enhance the research of ice lens criterion and provide a new perspective for monitoring the freezing process.
文摘Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.
基金Supported by the Guangxi Special Program for Technological Innovation Guidance(No.GuiKeAC25069006).
文摘In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability.
基金Project supported by the National Natural Science Foundation of China(Nos.12132015,12332015,and 12302333)。
文摘This study investigates the motion behavior of a slender flexible particle in a backward-facing step(BFS)flow using the direct-forcing fictitious domain method,with a particular focus on the trapping phenomena near the separation vortex region.Three distinct motion modes are identified:periodic rotation or oscillation within the vortex(trapping),downstream transport(escape),and transition state exhibiting unstable trapping.A dynamic balance among inward migration,centrifugal effects,wall interactions,and elastic forces enables the particle to achieve stable orbital motion within two distinct limit cycles.The topology of these orbits is governed by parameters,including the aspect ratio,structural flexibility,deformation intensity,and fluid inertia,all of which are characterized by the Reynolds number(Re).Specifically,fluid inertia plays a dominant role in facilitating particle trapping.At a fixed Re,a particle with a smaller aspect ratio tends to migrate inward and become trapped,whereas one with a larger aspect ratio is more likely to escape.Structural flexibility,especially when enhanced by confinement near the wall,leads to elastic deformation that induces secondary vortices and a weak flipping motion.The deformation intensityαsignificantly influences the lateral migration of the slender particle after the initial release;a largerαcauses it to drift toward the channel centerline,increasing the probability of escape.These findings provide a theoretical foundation for optimizing the transport and capture of slender soft swimmers in complex flow environments.
