Статті в журналах: "Particles tracking"

1

Jones, Benjamin T., Andrew Solow, and Rubao Ji. "Resource Allocation for Lagrangian Tracking." Journal of Atmospheric and Oceanic Technology 33, no. 6 (June 2016): 1225–35. http://dx.doi.org/10.1175/jtech-d-15-0115.1.

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AbstractAccurate estimation of the transport probabilities among regions in the ocean provides valuable information for understanding plankton transport, the spread of pollutants, and the movement of water masses. Individual-based particle-tracking models simulate a large ensemble of Lagrangian particles and are a common method to estimate these transport probabilities. Simulating a large ensemble of Lagrangian particles is computationally expensive, and appropriately allocating resources can reduce the cost of this method. Two universal questions in the design of studies that use Lagrangian particle tracking are how many particles to release and how to distribute particle releases. A method is presented for tailoring the number and the release location of particles to most effectively achieve the objectives of a study. The method detailed here is a sequential analysis procedure that seeks to minimize the number of particles that are required to satisfy a predefined metric of result quality. The study assesses the result quality as the precision of the estimates for the elements of a transport matrix and also describes how the method may be extended for use with other metrics. Applying this methodology to both a theoretical system and a particle transport model of the Gulf of Maine results in more precise estimates of the transport probabilities with fewer particles than from uniformly or randomly distributing particle releases. The application of this method can help reduce the cost of and increase the robustness of results from studies that use Lagrangian particles.

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2

Zhang, Lieping, Jinghua Nie, Shenglan Zhang, Yanlin Yu, Yong Liang, and Zuqiong Zhang. "Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization." Journal of Electrical and Computer Engineering 2021 (September 4, 2021): 1–8. http://dx.doi.org/10.1155/2021/2838971.

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Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

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3

Siradjuddin, Indah Agustien, and Muhammad Rahmat Widyanto. "Particle Filter with Gaussian Weighting for Vehicle Tracking." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 6 (August 20, 2011): 681–86. http://dx.doi.org/10.20965/jaciii.2011.p0681.

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To track vehicle motion in data video, particle filter with Gaussian weighting is proposed. This method consists of four main stages. First, particles are generated to predict target’s location. Second, certain particles are searched and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The proposed method could reduce computational time of tracking compared to that of conventional method of particle filter, since the proposed method does not have to calculate all particles weight using likelihood function. This method has been tested on video data with car as a target object. In average, this proposed method of particle filter is 60.61% times faster than particle filter method meanwhile the accuracy of tracking with this newmethod is comparable with particle filter method, which reach up to 86.87%. Hence this method is promising for real time object tracking application.

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4

Sun, Qi Yuan, Liu Sheng Li, and Zuo Liang Chao. "Target Tracking Based on Particle Filter with Multi-Path Particles." Applied Mechanics and Materials 130-134 (October 2011): 3306–10. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3306.

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Visual tracking is a key issue for autonomous navigation, intelligent monitoring system and so on. While a Particle Filter for tracking is designed, the target in image may be out of range, and the tracked target will be lost since the target image size keeps changing. A method for tracking a mobile target with visual image, Particle Filter with multi-path particles (PFWMP), is proposed to solve the above problem in this paper. Here, the method is based on the wavelet transform incorporated in traditional Particle Filter, and particles are made to move in original image and the image processed by wavelet transform. The results show that the improved approach using multiple particles can enhance the robustness of tracking algorithm and improve tracking accuracy.

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5

Li, Tao, and Qi Yuan Sun. "A Visual Tracking Based on Particle Filter of Multi-Algorithm Fusion." Applied Mechanics and Materials 513-517 (February 2014): 2893–96. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2893.

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A novel visual tracking algorithm based on particle filter with multi-algorithm fusion is proposed. Mean shift is employed to make particles distribute more reasonably in order to maintain tracking accuracy by using fewer particles, and the genetic evolution ideas is introduced to increase the diversity of samples by applying selection, crossover and mutation operator to achieve particles resampling. The experiments show that the tracking performance of the proposed method, compared with Mean Shift Embedded Particle Filter (MSEPF), is significantly improved.

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6

Wang, Lian-Ping, and D. E. Stock. "Numerical Simulation of Heavy Particle Dispersion Time Step and Nonlinear Drag Considerations." Journal of Fluids Engineering 114, no. 1 (March 1, 1992): 100–106. http://dx.doi.org/10.1115/1.2909983.

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Numerical experiments can be used to study heavy particle dispersion by tracking particles through a numerically generated instantaneous turbulent flow field. In this manner, data can be generated to supplement physical experiments. To perform the numerical experiments efficiently and accurately, the time step used when tracking the particles through the fluid must be chosen correctly. After finding a suitable time step for one particular simulation, the time step must be reduced as the total integration time increases and as the free-fall velocity of the particle increases. Based on the numerical calculations, we suggest that the nonlinear drag be included in a numerical simulation if the ratio of the particle’s Stokes free-fall velocity to the fluid rms velocity is greater than two.

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7

Müller, Dennis, Andreas Rausch, Olga Dolnik, and Thomas Schanze. "Comparing human and algorithmic tracking of subviral particles in fluorescence microscopic image sequences." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 543–47. http://dx.doi.org/10.1515/cdbme-2017-0114.

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AbstractTracking of subviral particles with automated methods enables the analysis of intracellular processes exhibited by viruses. A linear assignment problem solver and a Kalman-filter have been added to an existing particle tracking algorithm. First results produced with simulated image sequences showed that the improved algorithm is able to improve tracking results by closing gaps in the particle’s trajectories. Here we report on the evaluation of the LAP-Kalman algorithm using real fluorescence-microscopic images. The results from the original and improved algorithm have been compared to the results of manual tracking. Evaluation results indicate that the improved algorithm is capable to reconstruct missing parts of particle tracks in difficult conditions. However, the evaluation of the algorithms and the manual tracking is a complex task because of the low image contrast and high object density with intersecting tracks in the live-cell images.

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8

Yao, Hai Tao, Hai Qiang Chen, and Tuan Fa Qin. "Niche PSO Particle Filter with Particles Fusion for Target Tracking." Applied Mechanics and Materials 239-240 (December 2012): 1368–72. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1368.

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An improved particle filter algorithm is proposed to track a randomly moving target in video. In particle filter framework, a particle swarm optimization improved by niche technique which implemented by restricted competition selection is integrated. It can move particles into high likelihood area of target and form multi-population distribution, so that the searching capability of particles is enhanced and then the adaptation to the change of dynamic target state is improved. The particles of niching particle swarm optimization and the particles of particle filter are integrated for new particle weight calculation and finally realize a new particle filter for target tracking in video sequence.

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9

Zhu, Hong Bo, Hai Zhao, Dan Liu, and Chun He Song. "Compressed Iterative Particle Filter for Target Tracking." Applied Mechanics and Materials 55-57 (May 2011): 91–94. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.91.

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Particle filtering has been widely used in the non-linear n-Gaussian target tracking problems. The main problem of particle filtering is the lacking and exhausting of particles, and choosing effective proposed distribution is the key point to overcome it. In this paper, a new mixed particle filtering algorithm was proposed. Firstly, the unscented kalman filtering is used to generate the proposed distribution, and in the resample step, a new certain resample method is used to choose the particles with ordered larger weights. GA algorithm is introduced into the certain resample method to keep the variety of the particles. Simuational results have shown that the proposed algorithm has better performances than other three typical filtering algorithms.

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10

Chen, Zhimin, Mengchu Tian, Yuming Bo, and Xiaodong Ling. "Infrared small target detection and tracking algorithm based on new closed-loop control particle filter." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 4 (January 30, 2018): 1435–56. http://dx.doi.org/10.1177/0954410017753445.

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The problem of particle impoverishment could be always found in standard particle filter, additionally a large number of particles are required for accurate estimation. as it is difficult to meet the demand of modern infrared search and tracking system. To solve this problem, an improved infrared small target detection and tracking method based on closed-loop control bat algorithm optimized particle filter is proposed. Firstly, bat algorithm is introduced into the particle filtering in this method. Particles are used to simulate the process that an individual bat hunts and avoids obstacles so that particles move towards the high-likelihood region. Meanwhile, the improved algorithm takes the proportion of particles accepting a new state as the feedback quantity and proposes to conduct dynamic control on global and local search ability of particle filtering by closed-loop control strategy, which further improves the overall quality of particle distribution. The performance of the improved detection and tracking algorithm is tested in simulation scene and real scene of infrared small target. Experimental results show that the improved algorithm improves the performance of the infrared searching and tracking system.

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11

Schiepel, D., S. Herzog, R. Barta, and C. Wagner. "A Probabilistic Particle Tracking Framework For High Particle Densities." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (July 11, 2022): 1–10. http://dx.doi.org/10.55037/lxlaser.20th.43.

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A framework for particle tracking velocimetry at high particle densities (HD-PTV) based on a Gaussian Mixture Model (GMM) is presented. This new approach is validated by tracking synthetic particles generated for a generalized turbulent pipe flow defining the ground truth. For a step size per time step of δS = 14 px and a particles per pixel (ppp) density of 0.09 the framework tracks about 90% of the ground truth particles (percentage of matched particles, pmp) already after 9 time steps without generating any ghost particles. For a lower step size of δS = 7 px, corresponding to a higher temporal resolution of the flow, and the lowest investigated particle density ppp = 0.02 a constant pmp close to 100% is reported. A decrease on pmp to 80% is found for the highest ppp = 0,11 - corresponding to about 45000 particles in total. Increasing the step size per time step to δS = 14 px results in a similar sloping curve and pmp that are generally 5% lower compared to the lower step size. The approach is further successfully applied to a well-known experimental tracking problem, i.e. particle tracking in turbulent Rayleigh-Bénard convection, for which the motion of about 28500 particles is tracked. With track lengths up to 250 times steps the occuring structures and velocities are investigated and agree well with previous studies based on tomographic particle image velocimetry using the same data. Thus, it is concluded that the presented HD-PTV framework is an appropriate tool for the flow analysis even at high particle densities.

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12

Liu, Qiaoran, and Xun Yang. "Improved Interacting Multiple Model Particle Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (February 2018): 169–75. http://dx.doi.org/10.1051/jnwpu/20183610169.

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For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

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13

Takeyama, Mao, Kota Fujiwara, and Yasuo Hattori. "Improvement in the Number of Velocity Vector Acquisitions Using an In-Picture Tracking Method for 3D3C Rainbow Particle Tracking Velocimetry." Fluids 9, no. 10 (September 30, 2024): 226. http://dx.doi.org/10.3390/fluids9100226.

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Particle image velocimetry and particle tracking velocimetry (PTV) have developed from two-dimensional two-component (2D2C) velocity vector measurements to 3D3C measurements. Rainbow particle tracking velocimetry is a low-cost 3D3C measurement technique adopting a single color camera. However, the vector acquisition rate is not so high. To increase the number of acquired vectors, this paper proposes a high probability and long-term tracking method. First, particles are tracked in a raw picture instead of in three-dimensional space. The tracking is aided by the color information. Second, a particle that temporarily cannot be tracked due to particle overlap is compensated for using the positional information at times before and after. The proposed method is demonstrated for flow under a rotating disk with different particle densities and velocities. The use of the proposed method improves the tracking rate, number of continuous tracking steps, and number of acquired velocity vectors. The method can be applied under the difficult conditions of high particle density (0.004 particles per pixel) and large particle movement (maximum of 60 pix).

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14

Chang, L., G. Bourianoff, B. Cole, and S. Machida. "A Parallel Implementation of Particle Tracking with Space Charge Effects on an Intel iPSC/860." Scientific Programming 2, no. 3 (1993): 37–47. http://dx.doi.org/10.1155/1993/397679.

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Particle-tracking simulation is one of the scientific applications that is well suited to parallel computations. At the Superconducting Super Collider, it has been theoretically and empirically demonstrated that particle tracking on a designed lattice can achieve very high parallel efficiency on a MIMD Intel iPSC/860 machine. The key to such success is the realization that the particles can be tracked independently without considering their interaction. The perfectly parallel nature of particle tracking is broken if the interaction effects between particles are included. The space charge introduces an electromagnetic force that will affect the motion of tracked particles in three-dimensional (3-D) space. For accurate modeling of the beam dynamics with space charge effects, one needs to solve 3-D Maxwell field equations, usually by a particle-in-cell (PIC) algorithm. This will require each particle to communicate with its neighbor grids to compute the momentum changes at each time step. It is expected that the 3-D PIC method will degrade parallel efficiency of particle-tracking implementation on any parallel computer. In this paper, we describe an efficient scheme for implementing particle tracking with space charge effects on an INTEL iPSC/860 machine. Experimental results show that a parallel efficiency of 75% can be obtained.

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15

Kelly, D., R. D. Fischer, M. Moaven, S. Morris, B. C. Prorok, and B. Thurow. "Simultaneous Particle Tracking Velocimetry And Pyrometry Of Spatter Particles Ejected During The Laser Power-Bed Fusion Process Using A Spectral Plenoptic Camera." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (July 11, 2022): 1–18. http://dx.doi.org/10.55037/lxlaser.20th.81.

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This paper aims to provide a method for simultaneous 3D particle tracking velocimetry and dual-wavelength pyrometry using a single spectral plenoptic camera for uses in spray and flow diagnostics. The spectral plenoptic camera can capture spatial, angular, and spectral information of light from the scene using a single main lens and sensor. Particle tracking is conducted using the Light-Field Ray Bundling algorithm paired with a four-frame best estimate-enhanced track initialization algorithm to generate 3D high-resolution spatial locations and velocity of particles. Secondly, a dual-wavelength pyrometry method was developed to use particle locations from Light-Field Ray Bundling to find the particle spectra for each wavelength, and then calculate temperature using the spectral intensities. This method achieves 3D particle tracking and temperature measurements using a single lens and sensor, which can be applicable for many different flow measurements. The method was applied to the spatter particle generation caused by the Laser Powder-Bed Fusion process, where spatter particles have been found to be detrimental to the manufactured parts. The calculated speeds of the spatter particles are within the expected range, and the tracks show a complex 3D process. The temperature measurements indicate that the detected particles are in the liquid phase, with temperatures greater than 2000ºC. The simultaneous measurements demonstrate an overall deceleration and cooling of particles during their flight. Therefore, this is a viable technique for simultaneous tracking and temperature measurements.

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16

Cheng, Yi, Wenbo Ren, Chunbo Xiu, and Yiyang Li. "Improved Particle Filter Algorithm for Multi-Target Detection and Tracking." Sensors 24, no. 14 (July 20, 2024): 4708. http://dx.doi.org/10.3390/s24144708.

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In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

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17

Ratre, Avinash. "GMM-based Imbalanced Fractional Whale Particle Filter for Multiple Object Tracking in Surveillance Videos." International Journal of Computer Network and Information Security 17, no. 2 (April 8, 2025): 34–50. https://doi.org/10.5815/ijcnis.2025.02.03.

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The imbalanced surveillance video dataset consists of majority and minority classes as normal and anomalous instances in the nonlinear and non-Gaussian framework. The normal and anomalous instances cause majority and minority samples or particles associated with high and low probable regions when considering the standard particle filter. The minority particles tend to be at high risk of being suppressed by the majority particles, as the proposal probability density function (pdf) encourages the highly probable regions of the input data space to remain a biased distribution. The standard particle filter-based tracker afflicts with sample degeneration and sample impoverishment due to the biased proposal pdf ignoring the minority particles. The difficulty in designing the correct proposal pdf prevents particle filter-based tracking in the imbalanced video data. The existing methods do not discuss the imbalanced nature of particle filter-based tracking. To alleviate this problem and tracking challenges, this paper proposes a novel fractional whale particle filter (FWPF) that fuses the fractional calculus-based whale optimization algorithm (FWOA) and the standard particle filter under weighted sum rule fusion. Integrating the FWPF with an iterative Gaussian mixture model (GMM) with unbiased sample variance and sample mean allows the proposal pdf to be adaptive to the imbalanced video data. The adaptive proposal pdf leads the FWPF to a minimum variance unbiased estimator for effectively detecting and tracking multiple objects in the imbalanced video data. The fractional calculus up to the first four terms makes the FWOA a local and global search operator with inherent memory property. The fractional calculus in the FWOA oversamples minority particles to be diversified with multiple imputations to eliminate data distortion with low bias and low variance. The proposed FWPF presents a novel imbalance evaluation metric, tracking distance correlation for the imbalanced tracking over UCSD surveillance video data and shows greater efficacy in mitigating the effects of the imbalanced nature of video data compared to other existing methods. The proposed method also outshines the existing methods regarding precision and accuracy in tracking multiple objects. The consistent tracking distance correlation near zero values provides efficient imbalance reduction through bias-variance correction compared to the existing methods.

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18

Huo, Youhui, Yaohong Chen, Hongbo Zhang, Haifeng Zhang, and Hao Wang. "Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion." Electronics 11, no. 15 (August 7, 2022): 2457. http://dx.doi.org/10.3390/electronics11152457.

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Particle filters have been widely used in dim and small target tracking, which plays a significant role in navigation applications. However, their characteristics, such as difficulty of expressing features for dim and small targets and lack of particle diversity caused by resampling, lead to a considerable negative impact on tracking performance. In the present paper, we propose an improved resampling particle filter algorithm based on adaptive multi-feature fusion to address the drawbacks of particle filters for dim and small target tracking and improve the tracking performance. We first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform. We then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights, with the tracking accuracy and particle diversity improving simultaneously. The experimental results demonstrate that our proposed method achieves a high tracking performance with a distance accuracy of 77.2% and a running speed of 106 fps, respectively, meaning that it will have a promising prospect in dim and small target tracking applications.

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19

Zhao, Y., Y. Luo, W. Wang, S. Liu, and Z. Zhan. "Effect of base roughness on flow behavior and size segregation in bidisperse dry granular flow through PTV analysis." Géotechnique Letters 12, no. 4 (December 1, 2022): 1–23. http://dx.doi.org/10.1680/jgele.22.00044.

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Three bases with increasing roughness were designed by adjusting the geometric relationships of particles bonded to the base, to study the roughness effect on flow behaviors and size segregation in bidisperse granular chute flows. In light of limited research on accurate tracking of different particles in bidisperse granular flows, Particle Tracking Velocimetry (PTV), which allows tracking of large and small particles down to the pixel level, was used to measure the velocity and displacement of large and small particles separately. Experimental results show that when the critical inclination θc increases from 31.9° to 36.1°, the mixing point of the large and small particles decreases from 67 cm to 39 cm. Moreover, the overall flow is suppressed in the stream-wise direction and promoted in the depth-wise direction as the base roughness increases.

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20

Mazzaferri, Javier, Stephane Lefrancois, and Santiago Costantino. "Tracking Inhomogeneously Distributed Particles." Biophysical Journal 106, no. 2 (January 2014): 808a. http://dx.doi.org/10.1016/j.bpj.2013.11.4430.

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Yang, Hengye, Gregory P. Bewley, and Silvia Ferrari. "A Fast-Tracking-Particle-Inspired Flow-Aided Control Approach for Air Vehicles in Turbulent Flow." Biomimetics 7, no. 4 (November 6, 2022): 192. http://dx.doi.org/10.3390/biomimetics7040192.

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Natural phenomena such as insect migration and the thermal soaring of birds in turbulent environments demonstrate animals’ abilities to exploit complex flow structures without knowledge of global velocity profiles. Similar energy-harvesting features can be observed in other natural phenomena such as particle transport in turbulent fluids. This paper presents a new feedback control approach inspired by experimental studies on particle transport that have recently illuminated particles’ ability to traverse homogeneous turbulence through the so-called fast-tracking effect. While in nature fast tracking is observed only in particles with inertial characteristics that match the flow parameters, the new fast-tracking feedback control approach presented in this paper employs available propulsion and actuation to allow the vehicle to respond to the surrounding flow in the same manner as ideal fast-tracking particles would. The resulting fast-tracking closed-loop controlled vehicle is then able to leverage homogeneous turbulent flow structures, such as sweeping eddies, to reduce travel time and energy consumption. The fast-tracking approach is shown to significantly outperform existing optimal control solutions, such as linear quadratic regulator and bang-bang control, and to be robust to changes in the vehicle characteristics and/or turbulent flow parameters.

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Dodds, David, Abd Alhamid R. Sarhan, and Jamal Naser. "CFD Investigation into the Effects of Surrounding Particle Location on the Drag Coefficient." Fluids 7, no. 10 (October 17, 2022): 331. http://dx.doi.org/10.3390/fluids7100331.

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In the simulation of dilute gas-solid flows such as those seen in many industrial applications, the Lagrangian Particle Tracking method is used to track packets of individual particles through a converged fluid field. In the tracking of these particles, the most dominant forces acting upon the particles are those of gravity and drag. In order to accurately predict particle motion, the determination of the aforementioned forces become of the upmost importance, and hence an improved drag force formula was developed to incorporate the effects of particle concentration and particle Reynolds number. The present CFD study examines the individual effects of particles located both perpendicular and parallel to the flow direction, as well as the effect of a particle entrain within an infinite matrix of evenly distributed particles. Results show that neighbouring particles perpendicular to the flow (Model 2) have an effect of increasing the drag force at close separation distances, but this becomes negligible between 5–10 particle diameters depending on particle Reynolds number (Rep). When entrained in an infinite line of particles co-aligned with the flow (Model 1), the drag force is remarkably reduced at close separation distances and increases as the distance increases. The results of the infinite matrix of particles (Model 3) show that, although not apparent in the individual model, the effect of side particles is experienced many particle diameters downstream.

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Guo, Yan. "Moving Target Localization in Sports Image Sequence Based on Optimized Particle Filter Hybrid Tracking Algorithm." Complexity 2021 (June 5, 2021): 1–11. http://dx.doi.org/10.1155/2021/2643690.

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This paper proposes a fusion stadium positioning algorithm, which uses self-optimizing particle filter to integrate the improved athlete dead reckoning and WiFi position fingerprint algorithm for stadium positioning. In order to determine the initial absolute position of the stadium positioning, for athletes entering the stadium from the outside, a seamless switching algorithm outside the stadium is proposed, using the characteristics of high-altitude satellite GPS to find a suitable switching point as the initial absolute position. If in the stadium, WiFi static positioning determines the initial absolute position. Then, aiming at the problem that the poorly diversified particles cannot be better integrated and localized, a self-optimized particle filter algorithm is proposed. After resampling and retaining high-weight particles, the characteristics of low-weight particles are embedded in the copied high-weight particles. This can improve diversity, and we finally carry out fusion positioning. The target tracking algorithm based on Mean Shift has a fixed-scale tracking window, and the tracking effect of variable-size targets is not ideal. In this paper, an affine transformation algorithm is introduced to improve it. First, we iterate the adjacent image frames in reverse Mean Shift to determine the center position of the target and then use the corner matching method to perform template matching on the target to adjust the size of the tracking window. Through simulation verification, it is proved that the optimized particle filter hybrid tracking algorithm can achieve the ideal result when the target size changes. For the image sequence S1, the tracking window of the 20th frame and the 40th frame has a small offset, but the optimal position can be quickly found by Mean Shift iteration. For the image sequence S2, between the 40th frame and the 60th frame, the target occlusion causes the accuracy of the target template to decrease, and the Bhattacharyya coefficient is at a relatively low value. For the image sequence S3, the tracking effect of the optimized particle filter hybrid tracking algorithm meets the requirements.

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Du, Sichun, and Qing Deng. "Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking." Sensors 21, no. 6 (March 23, 2021): 2236. http://dx.doi.org/10.3390/s21062236.

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Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

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25

Wiharta, Dewa Made. "PARTICLE FILTER-BASED OBJECT TRACKING USING JOINT FEATURES OF COLOR AND LOCAL BINARY PATTERN HISTOGRAM FOURIER." Kursor 8, no. 2 (December 12, 2016): 79. http://dx.doi.org/10.28961/kursor.v8i2.64.

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Object tracking is defined as the problem of estimating object location in image sequences. In general, the problems of object tracking in real time and complex environtment are affected by many uncertainty. In this research we use a sequensial Monte Carlo method, known as particle filter, to build an object tracking algorithm. Particle filter, due to its multiple hypotheses, is known to be a robust method in object tracking task. The performances of particle filter is defined by how the particles distributed. The role of distribution is regulated by the system model being used. In this research, a modified system model is proposed to manage particles distribution to achieve better performance. Object representation also plays important role in object tracking. In this research, we combine color histogram and texture from Local Binary Pattern Histogram Fourier (LBPHF) operator as feature in object tracking. Our experiments show that the proposed system model delivers a more robust tracking task, especially for objects with sudden changes in speed and direction. The proposed joint feature is able to capture object with changing shape and has better accuracy than single feature of color or joint color texture from other LBP variants.

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26

Bansil, Rama, Joseph Hardcastle, and Maira Constantino. "Microrheology of Mucin: Tracking Particles and Helicobacter Pylori Bacteria." Epitoanyag - Journal of Silicate Based and Composite Materials 67, no. 4 (2015): 150–54. http://dx.doi.org/10.14382/epitoanyag-jsbcm.2015.25.

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27

Seng, Lok Bee, Muhammad Ahmar Zuber, Wan Mohd Faizal Wan Mahmood, Zulkhairi Zainol Abidin, and Zambri Harun. "Interpolation Techniques in Computational Particle Tracking inside a Direct-Injection Diesel Engine Cylinder." Applied Mechanics and Materials 663 (October 2014): 381–86. http://dx.doi.org/10.4028/www.scientific.net/amm.663.381.

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Radial Basis Function (RBF) interpolation and trilinear interpolation techniques are compared in the soot particle tracking inside the cylinder of a direct injection engine. The interpolation techniques are used separately in an efficient routine written in Matlab codes which is developed to track the movement or pathline of soot particles in the engine operation cycle ranged from inlet valve closing (IVC) to exhaust valve opening (EVO). Soot particles are treated as a massless body and in spherical shape which will move under the influence of bulk gases flow inside the cylinder. Movement of soot particles are examined through the selection factors of particle's initial coordinate (r,Ɵ,z) and soot concentration level at different instant crack angle. Results obtained from both interpolation techniques are compared and good agreement is achieved with some minor relative difference. However, RBF interpolation has wider applications potential where it can be applied to variety type of mesh geometry as compared to trilinear interpolation which is best used in mesh with hexahedral shape.

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28

Bendicks, Christian, Dominique Tarlet, Christoph Roloff, Robert Bordás, Bernd Wunderlich, Bernd Michaelis, and Dominique Thévenin. "Improved 3-D Particle Tracking Velocimetry with Colored Particles." Journal of Signal and Information Processing 02, no. 02 (2011): 59–71. http://dx.doi.org/10.4236/jsip.2011.22009.

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Cheng, Zhuang, Bo Zhou, and Jianfeng Wang. "Tracking particles in sands based on particle shape parameters." Advanced Powder Technology 31, no. 5 (May 2020): 2005–19. http://dx.doi.org/10.1016/j.apt.2020.02.033.

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30

Yao, Yao, Ihor Smal, Ilya Grigoriev, Anna Akhmanova, and Erik Meijering. "Deep-learning method for data association in particle tracking." Bioinformatics 36, no. 19 (July 6, 2020): 4935–41. http://dx.doi.org/10.1093/bioinformatics/btaa597.

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Abstract Motivation Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data. Results Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next. Comprehensive evaluations on datasets from the particle tracking challenge demonstrate the competitiveness of the proposed deep-learning method compared to the state of the art. Additional tests on real-time-lapse fluorescence microscopy images of various types of intracellular particles show the method performs comparably with human experts. Availability and implementation The software code implementing the proposed method as well as a description of how to obtain the test data used in the presented experiments will be available for non-commercial purposes from https://github.com/yoyohoho0221/pt_linking. Supplementary information Supplementary data are available at Bioinformatics online.

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31

Luo, Xiao, Jie Zhang, Handong Tan, Jiahao Jiang, Junda Li, and Weijia Wen. "Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning." Sensors 24, no. 8 (April 18, 2024): 2583. http://dx.doi.org/10.3390/s24082583.

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In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a z-axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.

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32

Panday, Sanjeeb Prasad. "Stereoscopic Correspondence of Particles for 3-Dimensional Particle Tracking Velocimetry by using Genetic Algorithm." Journal of the Institute of Engineering 12, no. 1 (March 6, 2017): 10–26. http://dx.doi.org/10.3126/jie.v12i1.16706.

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The genetic algorithm (GA) based stereo particle-pairing algorithm has been developed and applied to the spatial particle-pairing problem of the stereoscopic three-dimensional (3-D) PTV system. In this 3 D PTV system, particles viewed by two (or more than two) stereoscopic cameras with a parallax have to be correctly paired at every synchronized time step. This is important because the 3-D coordinates of individual particles cannot be computed without the knowledge of the correct stereo correspondence of the particles. In the present study, the GA algorithm is applied to the epipolar line proximity analysis for establishing correspondence of particles pairs between two co-instantaneous stereoscopic particles images, in order to compute the 3-D coordinates of every individual particle. The results are tested with various standard images and it’s found that the new strategy using GA works better than conventional particle pairing methods of 3-D particle tracking velocimetry for steoroscopic PTV. Journal of the Institute of Engineering, 2016, 12(1): 10-26

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33

Kang, Jian, Wei Kang, and Guo Sheng Rui. "Study on Velocity Constrained Particle Filters in Passive Tracking Applications." Applied Mechanics and Materials 20-23 (January 2010): 482–86. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.482.

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To improve the poor performance of the maneuvering target tracking, Particle Filters (PF) and velocity constrained Particle Filters (VCPF) are introduced to track the maneuvering target. According to the prior information, outrange particles are discarded during prediction step, and the distribution and the weight of particles are adjusted. Simulation result shows that VCPF can track the maneuvering target stablely. Furthermore, the convergence rate and track accuracy of the algorithm can also be effectively improved.

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34

Windows-Yule, C. R. K., J. P. K. Seville, A. Ingram, and D. J. Parker. "Positron Emission Particle Tracking of Granular Flows." Annual Review of Chemical and Biomolecular Engineering 11, no. 1 (June 7, 2020): 367–96. http://dx.doi.org/10.1146/annurev-chembioeng-011620-120633.

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Positron emission particle tracking (PEPT) is a noninvasive technique capable of imaging the three-dimensional dynamics of a wide variety of powders, particles, grains, and/or fluids. The PEPT technique can track the motion of particles with high temporal and spatial resolution and can be used to study various phenomena in systems spanning a broad range of scales, geometries, and physical states. We provide an introduction to the PEPT technique, an overview of its fundamental principles and operation, and a brief review of its application to a diverse range of scientific and industrial systems.

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35

Zhao, San Lung, Shen Zheng Wang, Hsi Jian Lee, and Hung I. Pai. "Using Cumulative Histogram Maps in an Adaptive Color-Based Particle Filter for Real-Time Object Tracking." Advanced Materials Research 121-122 (June 2010): 585–90. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.585.

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The study presents a human tracking system. To tracking a person, we adopt a particle filter as tracking kernel, since the method has proven successful for tracking in non-linear and non-Gaussian estimation. In a particle filter, a set of weighted particles represents the possible target sates. In this study, we measure the weight according to both the appearances of the target object and background scene to improve the discriminability between them. In our tracker, the appearances are modeled as color histogram, since it is scale and rotation invariant. However, the color histogram extraction for a large number of overlap regions is repeated redundantly and inefficiently. To speed up it, we reduce the cost for calculating overlapped regions by creating a cumulative histogram map for the processing image. The experimental results show that the tracker has the best precision improvement, and the tracking speed is 49.7 fps for 384 × 288 resolution, when we use 600 particles. The results show that the proposed method can be applied to a real-time human tracking system with high precision.

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36

Li, Liu Bai, Yan Ma, and Yuan Yuan Liang. "Particle-Filter Tracking of Motion Vector to Locate Objects and Pattern Matching over Particles Based on Mpeg2." Advanced Materials Research 225-226 (April 2011): 350–55. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.350.

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During process of objects tracking, problem of tracking box about marked objects is a major problem. Moreover, tracking of multi-objects are also difficult problems of objects tracking. This paper can tag object automatically through using motion vector of Mpeg2 to mark activities object of static video. Then, we extract multi-dimensional characteristics from initial goal of motion vector determined and made model. And accurately identify particles of larger weight to achieve purpose of accurately tracking objects through process of original value of particle filter matching observed value. As adopted methods of pattern classifying, so made feature matching between new particle and original particle are more accurate. The experiments show that the algorithm had good tracking performance and strong robustness.

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37

Birch, David M., and Nicholas Martin. "Tracer particle momentum effects in vortex flows." Journal of Fluid Mechanics 723 (April 16, 2013): 665–91. http://dx.doi.org/10.1017/jfm.2013.82.

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AbstractThe measurement of vortex flows with particle-image velocimetry (PIV) is particularly susceptible to error arising from the finite mass of the tracer particles, owing to the high velocities and accelerations typically experienced. A classical model of Stokes-flow particle transport is adopted, and an approximate solution for the case of particle transport within an axisymmetric, quasi-two-dimensional Batchelor $q$-vortex is presented. A generalized expression for the maximum particle tracking error is proposed for each of the velocity components, and the importance of finite particle size distributions is discussed. The results indicate that the tangential velocity component is significantly less sensitive to tracking error than the radial component, and that the conventional particle selection criterion (based on the particle Stokes number) may result in either over- or under-sized particles for a specified allowable error bound. Results were demonstrated by means of PIV measurements carried out in air and water using particles with very different properties.

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38

Kong, Hongshan, and Bin Yu. "A Moving Object Indoor Tracking Model Based on Semiactive RFID." Mathematical Problems in Engineering 2018 (December 25, 2018): 1–7. http://dx.doi.org/10.1155/2018/4812057.

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Aimed at the weak anti-interference and low accuracy problem of moving object indoor tracking based RFID, a moving object indoor tracking model based on semiactive RFID is presented. This model acquires scene location information through RFID low frequency triggers preinstalled, which can enhance the anti-interference ability. This model adopts an improved particle filter algorithm, which can increase the diversity of the particles, overcome the particle impoverishment, and reduce the tracking error. Simulation results indicate that the model can achieve better tracking performances. Compared with standard particle filter, the improved algorithm performance is better in the capability of tracking accuracy and robust and is more suitable for indoor tracking application in the complicated environments.

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39

CHETVERIKOV, DMITRY. "APPLYING FEATURE TRACKING TO PARTICLE IMAGE VELOCIMETRY." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 04 (June 2003): 487–504. http://dx.doi.org/10.1142/s0218001403002496.

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Particle Image Velocimetry (PIV) is a popular approach to flow visualization and measurement in hydro- and aerodynamic studies and applications. The fluid is seeded with particles that follow the flow and make it visible. Traditionally, correlation techniques have been used to estimate the displacements of the particles in a digital PIV sequence. These techniques are relatively time-consuming and noise-sensitive. Recently, an optical flow estimation technique developed in machine vision has been successfully used in Particle Image Velocimetry. Feature tracking is an alternative approach to motion estimation, whose application to PIV is proposed and studied in this paper. Two efficient feature tracking algorithms are customized and applied to PIV. The algorithmic solutions of the application are described. In particular, techniques for coherence filtering and interpolation of a velocity field are developed. To assess the proposed and the previous approaches, velocity fields obtained by the different methods are quantitatively compared for numerous synthetic and real PIV sequences. It is concluded that the tracking algorithms offer Particle Image Velocimetry a good alternative to both correlation and optical flow techniques.

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40

Taghavi, Mohammadrasoul, and Edwin A. Marengo. "Optical-Theorem-Based Holography for Target Detection and Tracking." Sensors 25, no. 7 (March 31, 2025): 2203. https://doi.org/10.3390/s25072203.

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The development of robust, real-time optical methods for the detection and tracking of particles in complex, multiple-scattering media is a problem of practical importance in a number of fields, including environmental monitoring, air quality assessment, and homeland security. In this paper, we develop a holographic, optical-theorem-based method for the detection of particles embedded in complex environments where wavefronts undergo strong multiple scattering. The proposed methodology is adaptive to a complex medium, which is integral to the sensing apparatus and thereby enables constant monitoring through progressive adaptation. This feature, along with the holographic nature of the developed approach, also renders (as a byproduct) real-time imaging capabilities for the continuous tracking of particles traversing the region under surveillance. In addition, the proposed methodology also enables the development of customized sensors that leverage a controllable complex multiple-scattering medium and the derived holographic sensing technology for real-time particle detection and tracking. We demonstrate, with the help of realistic computer simulations, holographic techniques capable of detecting and tracking small particles under such conditions and analyze the role of multiple scattering in enhancing detection performance. Potential applications include the identification of aerosolized biological substances, which is critical for biosecurity, and the rapid detection of hazardous airborne particles in confined or densely populated areas.

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41

Breuker, Horst, Hans Drevermann, Christoph Grab, Alphonse A. Rademakers, and Howard Stone. "Tracking and Imaging Elementary Particles." Scientific American 265, no. 2 (August 1991): 58–63. http://dx.doi.org/10.1038/scientificamerican0891-58.

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42

CHEN, HUIYING, and YOUFU LI. "OPTIMIZED PARTICLES FOR 3D TRACKING." International Journal of Humanoid Robotics 08, no. 04 (December 2011): 631–47. http://dx.doi.org/10.1142/s021984361100268x.

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3D visual tracking is useful for many applications. In this paper, we propose two different ways for different system configurations to optimize particle filter for enhancing 3D tracking performances. On the one hand, a new data fusion method is proposed to obtain the optimal importance density function for active vision systems. With this method, the importance density function in particle filter can be modified to represent posterior states by particle crowds in a better way. Thus, it makes the tracking system more robust to noise and outliers. On the other hand, we develop a method for reconfigurable vision systems to maximize the effective sampling size in particle filter, which consequentially helps to solve the degeneracy problem and minimize the tracking error. Simulation and experimental results verified the effectiveness of the proposed method.

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43

Sun, Mingyu. "Volume-tracking of subgrid particles." International Journal for Numerical Methods in Fluids 66, no. 12 (April 8, 2010): 1530–54. http://dx.doi.org/10.1002/fld.2331.

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44

Brunel, Marc, Lila Ouldarbi, Alexandre Fahy, and Gaële Perret. "3D-Tracking of Sand Particles in a Wave Flume Using Interferometric Imaging." Optics 3, no. 3 (August 22, 2022): 254–67. http://dx.doi.org/10.3390/opt3030025.

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We report the 3D-tracking of irregular sand particles in a wave flume using a cylindrical interferometric particle imaging set-up. The longitudinal position of each particle is deduced from the ellipticity of its speckle-like interferometric image. The size of a particle is determined from the analysis of the 2D Fourier transform of its defocused image. It is further possible to identify some rotation of the particles. Simulations accurately confirm the experimental determination of the different parameters (3D position and size of each particle).

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45

Yan, Chongzhe. "Fourier transform-based optimization of particle velocity estimation for noise reduction in tracking experiments." Advances in Engineering Innovation 16, no. 3 (March 28, 2025): None. https://doi.org/10.54254/2977-3903/2025.21829.

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High-frequency noise, often caused by system jitter and environmental factors, can obscure the true motion of particles. This study presents a Fourier transform-based particle velocity optimization framework designed to improve the accuracy of velocity estimation in particle tracking experiments. High-frequency noise, often caused by system jitter and environmental factors, can obscure the true motion of particles. To address this, we propose an adaptive low-pass filtering approach where the cutoff frequency is optimized through a numerical search algorithm to minimize the error between the filtered velocity and the ground truth trajectory. Our results demonstrate that an optimal cutoff frequency of approximately 1 Hz offers the best balance between noise reduction and signal preservation. The framework is further enhanced by its adaptability to different experimental conditions, making it applicable to a wide range of particle tracking scenarios. This approach offers a more effective solution for overcoming noise-related challenges in particle tracking, providing a valuable tool for precise motion analysis in various scientific fields.

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46

Messer, P. K., A.-K. Henß, D. C. Lamb, and J. Wintterlin. "A multiscale wavelet algorithm for atom tracking in STM movies." New Journal of Physics 24, no. 3 (March 1, 2022): 033016. http://dx.doi.org/10.1088/1367-2630/ac4ad5.

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Abstract High-speed scanning tunneling microscopy (STM) data have become available that provide movies of time-dependent surface processes. To track adsorbed atoms and molecules in such data automatic routines are required. We introduce a multiresolution wavelet particle detection algorithm for this purpose. To identify the particles, the images are decomposed by means of a discrete wavelet transform into wavelet planes of different resolutions. An ‘à trous’ low-pass filter is applied. The coefficients from the wavelet planes are filtered to remove noise. Wavelet planes with significant coefficients from the particles are multiplied, and the product is transformed into a binary particle mask. The precision of the method is tested with data sets of adsorbed CO molecules and O atoms on a Ru(0001) surface. The algorithm can safely detect and localize these particles with high precision, even in the presence of the enhanced noise characteristic for high-speed, constant-height STM data. By linking the particle positions, we obtain extended trajectories with a resolution of ∼0.5 Å or better allowing us to investigate the detailed motion of single atoms on a surface.

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47

Abdullahi Daniyan. "Robust Multi-Target Tracking with a Kalman-Gain CPHD Filter: Simulation and Experimental Validation." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (April 30, 2025): 1636–47. https://doi.org/10.30574/wjaets.2025.15.1.0369.

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We introduce a novel cardinalized implementation of the Kalman-gain-aided particle probability hypothesis density (KG-SMC-PHD) filter, extending it to form the Kalman-Gain Particle Cardinalized Probability Hypothesis Density (KG-SMC-CPHD) filter. This new approach significantly enhances multi-target tracking by combining the particle-based state correction mechanism with the propagation of both the PHD and target cardinality distribution. Unlike conventional particle filters that require a large number of particles for acceptable performance, our method intelligently corrects selected particles during the weight update stage, resulting in a more accurate posterior with substantially fewer particles. Through comprehensive evaluations on both simulated and experimental datasets, the KG-SMC-CPHD filter demonstrates superior robustness and accuracy, particularly in high-clutter environments and nonlinear target dynamics. Notably, it offers improved cardinality estimation and maintains the computational efficiency and performance advantages of its predecessor, the KG-SMC-PHD filter, making it a powerful tool for advanced multi-target tracking applications.

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48

Tomanovic, Ivan, Srdjan Belosevic, Aleksandar Milicevic, Nenad Crnomarkovic, and Dragan Tucakovic. "Numerical tracking of sorbent particles and distribution during gas desulfurization in pulverized coal-fired furnace." Thermal Science 21, suppl. 3 (2017): 759–69. http://dx.doi.org/10.2298/tsci160212196t.

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Furnace sorbent injection for sulfur removal from flue gas presents a challenge, as the proper process optimization is of crucial importance in order to obtain both high sulfur removal rates and good sorbent utilization. In the simulations a two-phase gas-particle flow is considered. Pulverized coal and calcium-based sorbent particles motion is simulated inside of the boiler furnace. It is important to determine trajectories of particles in the furnace, in order to monitor the particles heat and concentration history. A two-way coupling of the phases is considered ? influence of the gas phase on the particles, and vice versa. Particle-to-particle collisions are neglected. Mutual influence of gas and dispersed phase is modeled by corresponding terms in the transport equations for gas phase and the equations describing the particles turbulent dispersion. Gas phase is modeled in Eulerian field, while the particles are tracked in Lagrangian field. Turbulence is modeled by the standard k-? model, with additional terms for turbulence modulation. Distribution, dispersion and residence time of sorbent particles in the furnace have a considerable influence on the desulfurization process. It was shown that, by proper organization of process, significant improvement considering emission reduction can be achieved.

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49

Wehrman, Matthew D., Seth Lindberg, and Kelly M. Schultz. "Multiple particle tracking microrheology measured using bi-disperse probe diameters." Soft Matter 14, no. 28 (2018): 5811–20. http://dx.doi.org/10.1039/c8sm01098f.

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50

Liu, Xiulin, Jianyi Chen, Hao Cui, Xiao Ma, Hongbin Zhang, and Yongrui Shan. "Novel Efficiency Calculation Model Based on Fine Particle Tracking Behavior." Processes 12, no. 8 (August 15, 2024): 1710. http://dx.doi.org/10.3390/pr12081710.

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The underflow entrainment of fine particles occurs during the hydrocyclone separation process, resulting in a “fishhook” effect on grade efficiency. Traditional efficiency models fail to address this phenomenon. This study examines the tracking behavior of fine particles, using variations in centrifugal settling velocity to characterize separation performance. The effect of this behavior on particle separation is quantified through a tracking coefficient for small particles and an entrainment coefficient for large particles, together forming a novel efficiency calculation model. The experimental research shows that the new model is applicable for the efficiency calculation of particles with different shapes, and can calculate grade efficiency curves with fishhook segments. By comparing with the existing research results, the accuracy and universality of the new model have been demonstrated. This model facilitates the accurate computation of grade efficiency curves, thereby significantly enhancing the precision of efficiency calculations, which provides guidance for the design and selection of hydrocyclones.

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