Position Of Particle Research Articles

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.

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.

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.

Dynamics of inertial particles under velocity resetting

We investigate stochastic resetting in coupled systems involving two degrees of freedom, where only one variable is reset. The resetting variable, which we think of as hidden, indirectly affects the remaining observable variable via correlations. We derive the Fourier–Laplace transforms of the observable variable’s propagator and provide a recursive relation for all the moments, facilitating a comprehensive examination of the process. We apply this framework to inertial transport processes where we observe the particle position while the velocity is hidden and is being reset at a constant rate. We show that velocity resetting results in a linearly growing spatial mean squared displacement at later times, independently of reset-free dynamics, due to resetting-induced tempering of velocity correlations. General expressions for the effective diffusion and drift coefficients are derived as a function of the resetting rate. A non-trivial dependence on the rate may appear due to multiple timescales and crossovers in the reset-free dynamics. An extension that incorporates refractory periods after each reset is considered, where post-resetting pauses can lead to anomalous diffusive behavior. Our results are of relevance to a wide range of systems, such as inertial transport where the mechanical momentum is lost in collisions with the environment or the behavior of living organisms where stop-and-go locomotion with inertia is ubiquitous. Numerical simulations for underdamped Brownian motion and the random acceleration process confirm our findings.

Experimental study of the effect of particle–wall interactions on inertial particle dynamics in wall turbulence

Based on Voronoi analysis, the properties related to the near-wall motion of particles in a turbulent boundary layer were experimentally investigated via different release modes, with a friction Reynolds number $Re_\tau =3530$ . For high-inertia sand particles with Stokes number $St^+ \sim O(10^2\unicode{x2013}10^3)$ and a volume fraction $\varPhi _v \sim O(10^{-4})$ , particle image tracking velocimetry was used to determine the particle position and near-wall distribution properties. We established three particle release modes, including top-released, bottom overall-released and bottom partially released sand particles, under the same flow field conditions and calculated the differences in particle near-wall clustering and void properties. It was confirmed that wall effects (including collision and strike-splash) have a great influence on particle clustering and void behaviour near the wall. In the top-released sand particle and locally laid sand particle cases, particles bounced off the smooth walls and re-entered the carrier flow, causing significant clustering and sparsing of particles near the walls. In contrast, in the overall sand-laying case where the bottom wall was completely covered with sand particles, there is no apparent cluster or void phenomenon near the wall $(z/\delta &lt;0.12)$ and the particles are randomly distributed, due to the combined effect of particle impact and splashing. In addition, the clustering and voids of particles become more pronounced with increasing wall-normal distance in the three release modes, and the particle distribution shows some self-similarity at each flow layer. The probability density function of the concentration of cluster particles decreases following a ‘ $-5/3$ ’ power law. However, due to the particle–wall interaction, the probability density function gradually deviates from the ‘ $-5/3$ ’ power law.

Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm

With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time.

Elliptical magnetic trajectories of a point on the elliptical 2-sphere

Elliptical trajectories play numerous significant roles in mathematics and physics. The focus of this work is to analyze the trajectories of a point on the ellipsoid S a 1 , a 2 , a 3 2 while it is under the influence of a killing vector field K. For this purpose, we introduce the generalized Darboux frame and the variational vector fields of S a 1 , a 2 , a 3 2 . We were able to get the trajectory equations of the charged particle on the ellipsoid by utilizing the elliptical structure and elliptic inner and cross product. We adopted a clearer and more comprehensible strategy in this way. With the aid of a mathematical program, we were able to display the paths we had determined based on the particle's velocity vector's position in the magnetic field. These trajectories are spiral, helical, and elliptical.

Particle initialization effects on Lyman-α forest statistics in cosmological SPH simulations

Abstract Confronting measurements of the Lyman-α forest with cosmological hydrodynamical simulations has produced stringent constraints on models of particle dark matter and the thermal and ionization state of the intergalactic medium. We investigate the robustness of such models of the Lyman-α forest, focussing on the effect of particle initial conditions on the Lyman-α forest statistics in cosmological SPH simulations. We study multiple particle initialization algorithms in simulations that are designed to be identical in other respects. In agreement with the literature, we find that the correct linear theory evolution is obtained when a glass-like configuration is used for initial unperturbed gas particle positions alongside a regular grid configuration for dark matter particles and the use of non-identical initial density perturbations for gas and dark matter. However, we report that this introduces a large scale-dependent distortion in the one-dimensional Lyman-α transmission power spectrum at small scales (k &amp;gt; 0.05 s/km). The effect is close to 50 % at k ∼ 0.1 s/km, and persists at higher resolution. This can severely bias inferences in parameters such as the dark matter particle mass. By considering multiple initial conditions codes and their variations, we also study the impact of a variety of other assumptions and algorithmic choices, such as adaptive softening, background radiation density, particle staggering, and perturbation theory accuracy, on the matter power spectrum, the Lyman-α flux power spectrum, and the Lyman-α flux PDF. This work reveals possible pathways towards more accurate theoretical models of the Lyman-α forest to match the quality of upcoming measurements.

Evaluation of array capacitive sensor for local concentration measurement of gas–solid particles flow by coupled fields based on CFD-DEM

An array capacitive sensor adopting two excitation modes is designed for measuring local particle concentration in gas–solid two-phase flow. Considering the difficulty in obtaining instantaneous flow parameters under dynamic experiments, a 3D dynamic simulation model coupling gas–solid two-phase fluid and electrostatic fields is proposed to quantitatively evaluate the sensor performance. In the coupling model, Computational Fluid Dynamics and Discrete Element Method are applied to establish two-phase fluid field for particle micro-distribution. The method of defining particle shape and position is used to set permittivity distribution in electrostatic field for coupling gas–solid fluid field and electrostatic field of array capacitive sensor. Using the method of reconstructed image and data fitting, the performance of two excitation modes are evaluated under a typical dynamic flow with structure of stratified flow. Experimental result validates the simulation conclusions and proves the effectiveness of the coupling simulation model for evaluating capacitive sensors in gas–solid flow systems.