Position Of Particle Research Articles

Quantum Brownian motion induced by a scalar field in Einstein’s universe

The Brownian motion of a point particle induced by quantum vacuum fluctuations of a massless real scalar field in Einstein’s universe is studied. By assuming the small displacement condition, the dispersion in the momentum and position of a point particle coupled to the massless scalar field are obtained. As a consequence of the homogeneity and isotropy properties of the Einstein’s Universe, we find that all components of these physical observables are identical. We also examine divergent behaviors associated with the physical momentum and position dispersions, which we attribute to the IR1×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{1}\ imes $$\\end{document}S3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{3}$$\\end{document} compact topology of Einstein’s universe. Finally, based on the small displacement condition assumed, we analyze the limit of validity of our investigation.

Multi-Objective Particle Swarm Optimization Algorithm based on Position Vector Offset

In response to the issue that particle swarm optimization algorithms tend to fall into local optima when dealing with multi-objective optimization tasks, a multi-objective optimization algorithm based on particle swarm is proposed. This algorithm is based on the relationship between the position vectors of particles, changing the selection and movement strategies of particles to find the true Pareto front. Firstly, two additional position vectors are added around the iterating particles to enhance their search capability; then, a non-dominated vector archive is established to record the non-dominated solutions of the iterating particles and the additional position vectors, increasing particle diversity. Finally, additional position vectors with high fitness are selected to produce a shift in the iterating particle's position, accelerating particle convergence. Comparing this algorithm with dMOPSO, SMPSO, NMPSO, and MOPSOCD algorithms, simulation experiments show that the proposed PVSPSO algorithm has stronger optimization ability.

Field‐Free Region Scanning‐Based Magnetic Microcarrier Targeting in Multibifurcation Vessels

Navigation of microcarriers in complex environments as a vascular network remains an open challenge due to limited solutions for effective targeting strategy. Simultaneous real‐time visualization and manipulation of microcarriers at any depth in the human body is far to be achieved even though one of each task has been successfully proven. Herein, a novel targeting strategy is proposed that employs field‐free region (FFR) scanning to guide microcarriers through multiple bifurcations within a predefined vessel network. The main challenge of this method lies on how, where, and when to activate FFR to steer a particle to a desired direction, regardless of its spatial feedback. To achieve it, first, a mathematical model of particle motion in a vessel network is developed to predict particle behaviors and positions. Subsequently, an optimization algorithm is formulated to place FFR well‐coordinated around each bifurcation at a designated moment. The established solution for targeting a magnetic microcarrier is preemptively evaluated through finite element simulations and then successfully implemented in in vitro multibranched vessels.

Investigating the influence of sub-mesoscale current structures on Baltic Sea connectivity through a Lagrangian analysis

This study explores the impact of sub-mesoscale structures and vertical advection on the connectivity properties of the Baltic Sea using a Lagrangian approach. High-resolution flow fields from the General Estuarine Transport Model (GETM) were employed to compute Lagrangian trajectories, focusing on the influence of fine-scale structures on connectivity estimates. Six river mouths in the Baltic Sea served as initial positions for numerical particles, and trajectories were generated using flow fields with varying horizontal resolutions: 3D trajectories with 250m resolution as well as 2D trajectories with 250m and 1km resolutions. Several Lagrangian indices, such as mean transit time, arrival depths, and probability density functions of transit times, were analyzed to unravel the complex circulation of the Baltic Sea and highlight the substantial impact of sub-mesoscale structures on numerical trajectories. Results indicate that in 2D simulations, particles exhibit faster movement on the eastern side of the Gotland Basin in high-resolution compared to coarse-resolution simulations. This difference is attributed to the stronger coastal current in high-resolution compared to coarse-resolution simulations. Additionally, the study investigates the influence of vertical advection on numerical particle motion within the Baltic Sea, considering the difference between 3D and 2D trajectories. Findings reveal that denser water in the eastern and south-eastern areas significantly affects particle dispersion in 3D simulations, resulting in increased transit times. Conversely, regions in the North-western part of the basin accelerate particle movement in 3D compared to the 2D simulations. Finally, we calculated the average residence time of numerical particles exiting the Baltic Sea through the Danish strait. Results show an average surface layer residence time of approximately 790 days over an eight-year integration period, highlighting the relatively slow water circulation in the semi-enclosed Baltic Sea basin. This prolonged residence time emphasizes the potential for the accumulation of pollutants. Overall, the study underscores the pivotal role of fine-scale structures in shaping the connectivity of the Baltic Sea, with implications for understanding and managing environmental challenges in this unique marine ecosystem.

A hybrid particle swarm optimization algorithm for solving engineering problem

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function (f1-f13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${f}_{1}-{f}_{13}$$\\end{document}) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems.

A Dual-Particle Approach for Incompressible SPH Fluids

Tensile instability is one of the major obstacles to particle methods in fluid simulation, which would cause particles to clump in pairs under tension and prevent fluid simulation to generate small-scale thin features. To address this issue, previous particle methods either use a background pressure or a finite difference scheme to alleviate the particle clustering artifacts, yet still fail to produce small-scale thin features in free-surface flows. In this article, we propose a dual-particle approach for simulating incompressible fluids. Our approach involves incorporating supplementary virtual particles designed to capture and store particle pressures. These pressure samples undergo systematic redistribution at each time step, grounded in the initial positions of the fluid particles. By doing so, we effectively reduce tensile instability in standard SPH by narrowing down the unstable regions for particles experiencing tensile stress. As a result, we can accurately simulate free-surface flows with rich small-scale thin features, such as droplets, streamlines, and sheets, as demonstrated by experimental results.

Confined run-and-tumble particles with non-Markovian tumbling statistics.

Confined active particles constitute simple, yet realistic, examples of systems that converge into a nonequilibrium steady state. We investigate a run-and-tumble particle in one spatial dimension, trapped by an external potential, with a given distribution g(t) of waiting times between tumbling events whose mean value is equal to τ. Unless g(t) is an exponential distribution (corresponding to a constant tumbling rate), the process is non-Markovian, which makes the analysis of the model particularly challenging. We use an analytical framework involving effective position-dependent tumbling rates to develop a numerical method that yields the full steady-state distribution (SSD) of the particle's position. The method is very efficient and requires modest computing resources, including in the large-deviation and/or small-τ regime, where the SSD can be related to the the large-deviation function, s(x), via the scaling relation P_{st}(x)∼e^{-s(x)/τ}.

Rotation-invariant rapid TRISO-fueled pebble identification based on feature matching and point cloud registration

The Pebble-bed reactor (PBR) utilizing TRISO-fueled pebbles is a promising Gen-IV reactor design due to its inherent safety and thermal efficiency. Identification of pebbles exiting the core may provide IAEA with additional information enhancing PBR safeguards help validate software predicting pebble flow behavior and burnup, and optimize fuel usage strategies should granular fuel management be needed. We propose a rapid, accurate pebble identification method applicable to commercial PBRs. Our approach relies on the unique spatial distribution of TRISO particles associated with each pebble, which can be extracted through X-ray CT. We developed an algorithm to identify a single pebble from 100,000 pebbles and achieved 100% identification accuracy in 90,000 trials, with arbitrary rotations and high noise levels. Remarkably, the outermost TRISO particle positions within a 0.6-mm shell suffice for unique identification. With an average identification time of under 50s per pebble, our method is compatible with PBR operational demands.

Bifurcation of equilibrium positions for ellipsoidal particles in inertial shear flows between two walls

We conducted a systematic numerical investigation of spherical, prolate and oblate particles in an inertial shear flow between two parallel walls, using smoothed particle hydrodynamics (SPH). It was previously shown that above a critical Reynolds number, spherical particles experience a supercritical pitchfork bifurcation of the equilibrium position in shear flow between two parallel walls, namely that the central equilibrium position becomes unstable, leading to the emergence of two new off-centre stable positions (Fox et al., J. Fluid Mech., vol. 915, 2021). This phenomenon was unexpected given the symmetry of the system. In addition to confirming this finding, we found, surprisingly, that ellipsoidal particles can also return to the centre position from the off-centre positions when the particle Reynolds number is further increased, while spherical particles become unstable under this increased Reynolds number. By utilizing both SPH and the finite element method for flow visualization, we explained the underlining mechanism of this reverse of bifurcation by altered streamwise vorticity and symmetry breaking of pressure. Furthermore, we expanded our investigation to include asymmetric particles, a novel aspect that had not been previously modelled, and we observed similar trends in particle dynamics for both symmetric and asymmetric ellipsoidal particles. While further validation through laboratory experiments is necessary, our research paves the road for development of new focusing and separation methods for shaped particles.

Modelling deformable boundary by spherical particle for normal contact

The normal contact of the elastic spherical particle with deformable boundary is investigated in terms of the Discrete Element Method (DEM). The particle of the prescribed radius is moving under gravity and the initial velocity. The deformable boundary is treated as rigidly fixed spherical particle with variable elasticity modulus and variable radius. The limit case, approaching the infinite radius presents an elastic half-space, while increasing of the elasticity modulus presents the rigid boundary, respectively.
 The linear model and the nonlinear Hertz contact model used in the discrete element method are investigated numerically by applying the 5th-order Gear’s predictorcorrector integration scheme. The numerical model is tested by comparing it with analytical solution. The time variations of the particle positions, velocities and accelerations are presented. On the basis of simulation results the limit values of the boundary particle parameters are evaluated and recommendations for the boundary article parameters required in DEM simulation are drown.

Analysis of Chloride Contaminant Transport in Tailings Storage Facility Dam (Case Study: Gold Mine in Sumatra)

In the practice of gold mining industry, hazardous waste known as tailings is produced during the ore extraction process. These tailings are typically stored in a dam structure called a Tailings Storage Facility (TSF). The planning and construction of a TSF are critical considerations, as the failure of a TSF can have substantial environmental impacts, pose risks to human safety, and result in industrial losses. Therefore, strict control is necessary in the development of TSFs to minimize the potential negative consequences. This research focuses on the transport of contaminants within a TSF, specifically examining the concentration of chloride contaminants and conducting particle movement analysis. The study utilizes modeling through the GeoStudio SEEP/W program to simulate groundwater flow profiles and the GeoStudio CTRAN/W program to understand contaminant movement over a 100-year period. GeoStudio modeling employs 10 materials: impermeable clay soil, filter sandy soil, transition gravel rock, three mine waste types (Fine, Rockfill, and Rockfill with fine), hard rock bedrock layer, in-situ soil representing the original layer, landslide with colluvial soil, and the tailings itself. Back analysis is employed to iterate model parameters and ensure modeling accuracy against field data, including comparisons with water quality test results and readings from vibrating wire piezometer (VWP) instrumentation. The contaminant transport is influenced by advection-dispersion processes and tends to concentrate within the TSF boundary toward the dam toe over a 100-year timeframe. The analysis emphasizes the influence of advection in contaminant transport and underscores the importance of particle position relative to the groundwater level, with Particle Tracking Analysis shows significant movement within the groundwater flow area. This research provides crucial insights into the dynamics of contaminant concentration, informing better decision-making in TSF planning and management. The findings underscore the imperative of strict control measures to minimize environmental impacts and human safety risks associated with TSFs, thereby advancing knowledge in gold mining waste management.

Refractive index sensing using quasi-bound states in the continuum in silicon metasurfaces

This work presents a bulk refractive index sensor based on quasi-bound states in the continuum (BICs) induced by broken symmetries in metasurfaces. The symmetry is broken by detuning the size and position of silicon particles periodically arranged in an array, resulting in multiple quasi-BIC resonances. We investigate the sensing characteristics of each of the resonances by measuring the spectral shift in response to changes in the refractive index of the surrounding medium. In addition, we reveal the sensing range of the different resonances through simulations involving a layer of deviating refractive index of increasing thickness. Interestingly, the resonances show very different responses, which we describe via the analysis of the near-field. This work contributes to the development of highly sensitive and selective BIC-based sensors that can be used for a wide range of applications.

Shell game with a quantum pea

We examine the role of quantum measurement and coherence, considering the shell game with a quantum particle. The game consists of three sites arranged to form a ring geometry and a particle visiting the sites through a unitary evolution. A player selects one of the sites as a target site and wins if the target site contains the particle at a preassigned time. The player has two options for determining win or loss: local and global measurements, which perform projective measurements on the occupancy of the target site and the particle's position, respectively. The game can be played over multiple rounds, and each round begins with the state reached at the end of the previous round. We calculate the probability of winning at each round and the probability of consecutive losses and find that those probabilities have characteristic asymptotes. We obtain the analytic expressions of the asymptote and scrutinize their essential dependence on the measurement options and coherence in the initial ensemble.

Directional change during active diffusion of viral ribonucleoprotein particles through cytoplasm

A mesh of cytoskeletal fibers, consisting of microtubules, intermediate filaments, and fibrous actin, prevents the Brownian diffusion of particles with a diameter larger than 0.10 μm, such as vesicular stomatitis virus ribonucleoprotein (RNP) particles, in mammalian cells. Nevertheless, RNP particles do move in random directions but at a lower rate than Brownian diffusion, which is thermally driven. This nonthermal biological transport process is called “active diffusion” because it is driven by ATP. The ATP powers motor proteins such as myosin II. The motor proteins bend and cross-link actin fibers, causing the mesh to jiggle. Until recently, little was known about how RNP particles get through the mesh. It has been customary to analyze the tracks of particles like RNPs by computing the slope of the ensemble-averaged mean-squared displacement of the particles as a signature of mechanism. Although widely used, this approach “loses information” about the timing of the switches between physical mechanisms. It has been recently shown that machine learning composed of variational Bayesian analysis, Gaussian mixture models, and hidden Markov models can use “all the information” in a single track to reveal that that the positions of RNP particles are spatially clustered. Machine learning assigns a number, called a state, to each cluster. RNP particles remain in one state for 0.2–1.0 s before switching (hopping) to a different state. This earlier work is here extended to analyze the movements of a particle within a state and to determine particle directionality within and between states.

Investigation on the effect of particle parameters on the erosion and erosion prediction model of the Pelton turbine

The Pelton turbine will play a massive role in China's water conservancy and power generation development process. In practical engineering applications, sediment will erode the components of the Pelton turbine when they come into contact, threatening the safe and stable operation of the unit. Therefore, based on the Eulerian–Lagrangian method, this study analyzes the effects of particle size, concentration, and position angle on the degree and distribution of erosion of each component without considering the cavitation effect of the flow. The results show that the larger the particle size, the more concentrated the distribution, and the more severe the abrasion caused on the spray needle and water bucket. However, the degree of nozzle erosion weakens, and the erosion area increases. It was also found that the higher the concentration, the more severe the abrasion caused to each component. When the jet completely hits the position on the bucket, due to the low impact speed, the impact angle remains almost unchanged, resulting in less erosion. Finally, the classic Finnie model was refined by adjusting the average erosion rate, particle size, concentration, and rotation angle. This modification yielded an enhanced model, mainly showcasing improved performance for moderate particle sizes.

Prediction of constrained modulus for granular soil using 3D discrete element method and convolutional neural networks

To efficiently predict the mechanical parameters of granular soil based on its random micro-structure, this study proposed a novel approach combining numerical simulation and machine learning algorithms. Initially, 3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional (3D) discrete element method (DEM) were conducted to construct a database. In this process, the positions of the particles were randomly altered, and the particle assemblages changed. Interestingly, besides confirming the influence of particle size distribution parameters, the stress-strain curves differed despite an identical gradation size statistic when the particle position varied. Subsequently, the obtained data were partitioned into training, validation, and testing datasets at a 7:2:1 ratio. To convert the DEM model into a multi-dimensional matrix that computers can recognize, the 3D DEM models were first sliced to extract multi-layer two-dimensional (2D) cross-sectional data. Redundant information was then eliminated via gray processing, and the data were stacked to form a new 3D matrix representing the granular soil's fabric. Subsequently, utilizing the Python language and Pytorch framework, a 3D convolutional neural networks (CNNs) model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil's fabric. The mean squared error (MSE) function was utilized to assess the loss value during the training process. When the learning rate (LR) fell within the range of 10-5–10-1, and the batch sizes (BSs) were 4, 8, 16, 32, and 64, the loss value stabilized after 100 training epochs in the training and validation dataset. For BS = 32 and LR = 10-3, the loss reached a minimum. In the testing set, a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error (MAPE) of 4.43% under the optimized condition, demonstrating the accuracy of this approach. Thus, by combining DEM and CNNs, the variation of soil's mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.

Cognitive Flexibility Moderates the Predictive Effect of Phonological Awareness on Focus Structures in Chinese Preschool Children.

Focus structures, a complex aspect of information structure in language, have garnered significant attention in psycholinguistics. The question of whether Chinese preschoolers aged 4-6 years possess the ability to process focus structures in oral communication, and how cognitive factors influence this ability, remains a research focal point. To address this, we recruited 100 Chinese preschoolers aged 4-6 years as participants in our study. This study manipulated the positions of focus particles in sentences to investigate the impact of phonological awareness on young children's comprehension of focus structures. Additionally, we examined the mediating roles of cognitive flexibility and inhibitory control. Our findings indicate the following: (1) phonological awareness positively predicted the accuracy of focus structural processing; (2) inhibitory control did not significantly predict the accuracy of focus structural processing; and (3) cognitive flexibility partially mediated the relationship between phonological awareness and focus structural comprehension. These results confirmed the predictive effect of cognitive flexibility on children's comprehension of focus structures. Moreover, they demonstrate that young children's phonological awareness can predict their focus structure comprehension ability through the mediating role of cognitive flexibility. This suggests that children's cognitive flexibility can aid in understanding sentences with focus structures.

First experimental time-of-flight-based proton radiography using low gain avalanche diodes

Objective. Ion computed tomography (iCT) is an imaging modality for the direct determination of the relative stopping power (RSP) distribution within a patient’s body. Usually, this is done by estimating the path and energy loss of ions traversing the scanned volume utilising a tracking system and a separate residual energy detector. This study, on the other hand, introduces the first experimental study of a novel iCT approach based on time-of-flight (TOF) measurements, the so-called Sandwich TOF-iCT concept, which in contrast to any other iCT systems, does not require a residual energy detector for the RSP determination. Approach. A small Sandwich TOF-iCT demonstrator was built based on low gain avalanche diodes (LGADs), which are 4D-tracking detectors that allow to simultaneously measure the particle position and time-of-arrival with a precision better than 100 μm and 100 ps, respectively. Using this demonstrator, the material and energy-dependent TOF was measured for several homogeneous PMMA slabs in order to calibrate the acquired TOF against the corresponding water equivalent thickness (WET). With this calibration, two proton radiographs (pRads) of a small aluminium stair phantom were recorded at MedAustron using 83 MeV and 100.4 MeV protons. Main results. Due to the simplified WET calibration models used in this very first experimental study of this novel approach, the difference between the measured and theoretical WET ranged between 37.09% and 51.12%. Nevertheless, the first TOF-based pRad was successfully recorded showing that LGADs are suitable detector candidates for Sandwich TOF-iCT. Significance. While the system parameters and WET estimation algorithms require further optimization, this work was an important first step to realize Sandwich TOF-iCT. Due to its compact and cost-efficient design, Sandwich TOF-iCT has the potential to make iCT more feasible and attractive for clinical application, which, eventually, could enhance the treatment planning quality.

A polynomial model with line-of-sight constraints for Lagrangian particle tracking under interface refraction

This paper introduces an improvement of the ‘shake-the-box (STB)’ (Schanz, Gesemann, and Schröder, Exp. Fluids 57.5, 2016) technique using the polynomial calibration model and the line-of-sight constraints (LOSC) to overcome the refractive interface issues in Lagrangian particle tracking (LPT) measurement. The method (named LOSC-LPT) draws inspiration from the two-plane polynomial camera calibration in tomographic particle image velocimetry (Worth, Nickels, Thesis, 2010) and the STB-based open-source Lagrangian particle tracking (OpenLPT) framework (Tan, Salibindla, Masuk, and Ni, Exp. Fluids 61.2, 2019). The LOSC-LPT introduces polynomial mapping functions into STB calibration in conditions involving gas–solid–liquid interfaces at container walls exhibiting large refractive index variations, which facilitates the realization of particle stereo matching, three-dimensional (3D) triangulation, iterative particle reconstruction, and further refinement of 3D particle position by shaking the LOS. Performance evaluation based on synthetic noise-free images with a particle image density of 0.05 particle per pixel in the presence of refractive interfaces demonstrates that LOSC-LPT can detect a higher number of particles and exhibits lower position uncertainty in the reconstructed particles, resulting in higher accuracy and robustness than that achieved with OpenLPT. In the application to an elliptical jet flow in an octagonal tank with refractive interfaces, the use of polynomial mapping results in smaller errors (mean calibration error <0.1 px) and thus more long trajectories identified by LOSC-LPT (13 000) compared with OpenLPT (4500) which uses the pinhole Tsai model (mean calibration error >1.0 px). Moreover, 3D flow-field reconstructions demonstrate that the LOSC-LPT framework can recover a more accurate 3D Eulerian flow field and capture more complete coherent structures in the flow, and thus holds great potential for widespread application in 3D experimental fluid measurements.

Depth from defocus technique: a simple calibration-free approach for dispersion size measurement

Particle size measurement is crucial in various applications, be it sizing droplets in inkjet printing or respiratory events, tracking particulate ejection in hypersonic impacts or detecting floating target markers in free-surface flows. Such systems are characterised by extracting quantitative information like size, position, velocity and number density of the dispersed particles, which is typically non-trivial. The existing methods like phase Doppler or digital holography offer precise estimates at the expense of complicated systems, demanding significant expertise. We present a novel volumetric measurement approach for estimating the size and position of dispersed spherical particles that utilises a unique ‘Depth from Defocus’ (DFD) technique with a single camera. The calibration-free sizing enables in situ examination of hard to measure systems, including naturally occurring phenomena like pathogenic aerosols, pollen dispersion or raindrops. The efficacy of the technique is demonstrated for diverse sparse dispersions, including dots, glass beads and spray droplets. The simple optical configuration and semi-autonomous calibration procedure make the method readily deployable and accessible, with a scope of applicability across vast research horizons.