Background Reduction of the particle size of corn increases energy digestibility and concentrations of digestible and metabolizable energy.Pelleting may also reduce particle size of grain,but it is not known if there ...Background Reduction of the particle size of corn increases energy digestibility and concentrations of digestible and metabolizable energy.Pelleting may also reduce particle size of grain,but it is not known if there are interactions between particle size reduction and pelleting.The objective of this experiment was to test the hypothesis that particle size reduction and pelleting,separately or in combination,increase N balance,apparent total tract digestibility(ATTD)of fiber and fat,and net energy(NE)in corn-soybean meal diets fed to group-housed pigs.Methods Six corn-soybean meal-based diets were used in a 3×2 factorial design with 3 particle sizes of corn(i.e.,700,500,or 300μm)and 2 diet forms(i.e.,meal or pelleted).Pigs were allowed ad libitum access to feed and water.Twenty-four castrated male pigs(initial weight:29.52 kg;standard diviation:1.40)were allotted to the 6 diets using a 6×6 Latin square design with 6 calorimeter chambers(i.e.,4 pigs/chamber)and 6 periods.Oxygen consumption and CO_(2)and CH_(4)productions were measured during fed and fasting states and fecal and urine samples were collected.Results Regardless of particle size of corn,the ATTD of gross energy(GE),N,and acid-hydrolyzed ether extract(AEE),and the concentration of NE were greater(P<0.05)in pelleted diets than in meal diets.Regardless of diet form,the ATTD of GE,N,and AEE,and the concentration of NE were increased(linear;P<0.05)by reducing the particle size of corn,but the increase was greater in meal diets than in pelleted diets(interaction;P<0.05).Conclusions Both pelleting and reduction of corn particle size increased nutrient digestibility and NE,but increases were greater in meal diets than in pelleted diets.展开更多
The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this s...The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.展开更多
The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy ...The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.展开更多
The homogeneous/particulate fluidization flow regime is particularly suitable for handling the various gas–solid contact processes encountered in the chemical and energy industry.This work aimed to extend such a regi...The homogeneous/particulate fluidization flow regime is particularly suitable for handling the various gas–solid contact processes encountered in the chemical and energy industry.This work aimed to extend such a regime of Geldart-A particles by exerting the axial uniform and steady magnetic field.Under the action of the magnetic field,the overall homogeneous fluidization regime of Geldart-A magnetizable particles became composed of two parts:inherent homogeneous fluidization and newly-created magnetic stabilization.Since the former remained almost unchanged whereas the latter became broader as the magnetic field intensity increased,the overall homogeneous fluidization regime could be extended remarkably.As for Geldart-A nonmagnetizable particles,certain amount of magnetizable particles had to be premixed to transmit the magnetic stabilization.Among others,the mere addition of magnetizable particles could broaden the homogeneous fluidization regime.The added content of magnetizable particles had an optimal value with smaller/lighter ones working better.The added magnetizable particles might raise the ratio between the interparticle force and the particle gravity.After the magnetic field was exerted,the homogeneous fluidization regime was further expanded due to the formation of magnetic stabilization flow regime.The more the added magnetizable particles,the better the magnetic performance and the broader the overall homogeneous fluidization regime.Smaller/lighter magnetizable particles were preferred to maximize the magnetic performance and extend the overall homogeneous fluidization regime.This phenomenon could be ascribed to that the added magnetizable particles themselves became more Geldart-A than-B type as their density or size decreased.展开更多
In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parame...In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.展开更多
The inward particle transport is associated with the formation of peaked density profiles,which contributes to improve the fusion rate and the realization of steady-state discharge.The active control of inward particl...The inward particle transport is associated with the formation of peaked density profiles,which contributes to improve the fusion rate and the realization of steady-state discharge.The active control of inward particle transport is considered as one of the most critical issues of magnetic confinement fusion.Recently,it is realized preliminarily by adding a biased endplate in the Peking University Plasma Test(PPT)device.The results reveal that the inward particle flux increases with the bias voltage of the endplate.It is also found that the profile of radial electric field(Er)shear is flattened by the increased bias voltage.Radial velocity fluctuations affect the inward particle more than density fluctuations,and the frequency of the dominant mode driving inward particle flux increases with the biased voltage applied to the endplate.The experimental results in the PPT device provide a method to actively control the inward particle flux using a biased endplate and enrich the understanding of the relationship between E_(r)×B shear and turbulence transport.展开更多
The breakage of brittle particulate materials into smaller particles under compressive or impact loads can be modelled as an instantiation of the population balance integro-differential equation.In this paper,the emer...The breakage of brittle particulate materials into smaller particles under compressive or impact loads can be modelled as an instantiation of the population balance integro-differential equation.In this paper,the emerging computational science paradigm of physics-informed neural networks is studied for the first time for solving both linear and nonlinear variants of the governing dynamics.Unlike conventional methods,the proposed neural network provides rapid simulations of arbitrarily high resolution in particle size,predicting values on arbitrarily fine grids without the need for model retraining.The network is assigned a simple multi-head architecture tailored to uphold monotonicity of the modelled cumulative distribution function over particle sizes.The method is theoretically analyzed and validated against analytical results before being applied to real-world data of a batch grinding mill.The agreement between laboratory data and numerical simulation encourages the use of physics-informed neural nets for optimal planning and control of industrial comminution processes.展开更多
The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optim...The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.展开更多
In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamm...In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.展开更多
In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a gene...In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a generalised Hellinger-Reissner(HR)variational principle,creating an implicit PFEM formulation.To mitigate the volumetric locking issue in low-order elements,we employ a node-based strain smoothing technique.By discretising field variables at the centre of smoothing cells,we achieve nodal integration over cells,eliminating the need for sophisticated mapping operations after re-meshing in the PFEM.We express the discretised governing equations as a min-max optimisation problem,which is further reformulated as a standard second-order cone programming(SOCP)problem.Stresses,pore water pressure,and displacements are simultaneously determined using the advanced primal-dual interior point method.Consequently,our numerical model offers improved accuracy for stresses and pore water pressure compared to the displacement-based PFEM formulation.Numerical experiments demonstrate that the N-PFEM efficiently captures both transient and long-term hydro-mechanical behaviour of saturated soils with high accuracy,obviating the need for stabilisation or regularisation techniques commonly employed in other nodal integration-based PFEM approaches.This work holds significant implications for the development of robust and accurate numerical tools for studying saturated soil dynamics.展开更多
Interparticle adhesion force has a controlling effect on the physical and mechanical properties of planetary regolith and rocks.The current research on the adhesion force of planetary regolith and rock particles has b...Interparticle adhesion force has a controlling effect on the physical and mechanical properties of planetary regolith and rocks.The current research on the adhesion force of planetary regolith and rock particles has been primarily based on the assumption of smooth spherical particles to calculate the intergranular adhesion force;this approach lacks consideration for the adhesion force between irregular shaped particles.In our study,an innovative approach was established to directly measure the adhesion force between the arbitrary irregular shaped particles;the probe was modified using simulated lunar soil particles that were a typical representation of planetary regolith.The experimental results showed that for irregular shaped mineral particles,the particle size and mineral composition had no significant influence on the interparticle adhesion force;however,the complex morphology of the contact surface predominantly controlled the adhesion force.As the contact surface roughness increased,the adhesion force gradually decreased,and the rate of decrease gradually slowed;these results were consistent with the change trend predicted via the theoretical models of quantum electrodynamics.Moreover,a theoretical model to predict the adhesion force between the irregular shaped particles was constructed based on Rabinovich’s theory,and the prediction results were correlated with the experimental measurements.展开更多
To meet the requirements of safety, concealment, and timeliness of trajectory planning during the unmanned aerial vehicle(UAV) penetration process, a three-dimensional path planning algorithm is proposed based on impr...To meet the requirements of safety, concealment, and timeliness of trajectory planning during the unmanned aerial vehicle(UAV) penetration process, a three-dimensional path planning algorithm is proposed based on improved holonic particle swarm optimization(IHPSO). Firstly, the requirements of terrain threat, radar detection, and penetration time in the process of UAV penetration are quantified. Regarding radar threats, a radar echo analysis method based on radar cross section(RCS)and the spatial situation is proposed to quantify the concealment of UAV penetration. Then the structure-particle swarm optimization(PSO) algorithm is improved from three aspects.First, the conversion ability of the search strategy is enhanced by using the system clustering method and the information entropy grouping strategy instead of random grouping and constructing the state switching conditions based on the fitness function.Second, the unclear setting of iteration numbers is addressed by using particle spacing to create the termination condition of the algorithm. Finally, the trajectory is optimized to meet the intended requirements by building a predictive control model and using the IHPSO for simulation verification. Numerical examples show the superiority of the proposed method over the existing PSO methods.展开更多
Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to ...Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.展开更多
At present Bayesian Networks(BN)are being used widely for demonstrating uncertain knowledge in many disciplines,including biology,computer science,risk analysis,service quality analysis,and business.But they suffer fr...At present Bayesian Networks(BN)are being used widely for demonstrating uncertain knowledge in many disciplines,including biology,computer science,risk analysis,service quality analysis,and business.But they suffer from the problem that when the nodes and edges increase,the structure learning difficulty increases and algorithms become inefficient.To solve this problem,heuristic optimization algorithms are used,which tend to find a near-optimal answer rather than an exact one,with particle swarm optimization(PSO)being one of them.PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds(how they search for food).PSO is employed widely because it is easier to code,converges quickly,and can be parallelized easily.We use a recently proposed version of PSO called generalized particle swarm optimization(GEPSO)to learn bayesian network structure.We construct an initial directed acyclic graph(DAG)by using the max-min parent’s children(MMPC)algorithm and cross relative average entropy.ThisDAGis used to create a population for theGEPSO optimization procedure.Moreover,we propose a velocity update procedure to increase the efficiency of the algorithmic search process.Results of the experiments show that as the complexity of the dataset increases,our algorithm Bayesian network generalized particle swarm optimization(BN-GEPSO)outperforms the PSO algorithm in terms of the Bayesian information criterion(BIC)score.展开更多
A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization.The primary a...A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization.The primary aim of MANETs is to extendflexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing system.An Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/violations.An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks,sending out alerts whenever it detects malicious nodes.The impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research article.The Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET environment.MANET system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node trustworthiness.Furthermore,KF is used to eliminate synchronisation errors that arise during the sending and receiving data.In order to provide an energy-efficient solution and to minimize network traffic,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in nodes.According to the researchfindings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.展开更多
Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to ...Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to the size of the memory,less storage capacity is experienced due to the restricted size of the respective node.In this proposed work,we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization(PSO).PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space.PSO is integrated with a Reduced Coverage Set,to obtain an optimal path with only high-power transmitting nodes.Energy Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution,to cover the whole network.The proposed algorithm has experimented with a different number of nodes.Comparison has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18%~40%,thereby increasing the network lifetime.展开更多
In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distribut...In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distributed generation(DG)based on renewable energy is critical for active distribution network operation enhancement.To comprehensively analyze the accessing impact of DG in distribution networks from various parts,this paper establishes an optimal DG location and sizing planning model based on active power losses,voltage profile,pollution emissions,and the economics of DG costs as well as meteorological conditions.Subsequently,multiobjective particle swarm optimization(MOPSO)is applied to obtain the optimal Pareto front.Besides,for the sake of avoiding the influence of the subjective setting of the weight coefficient,the decisionmethod based on amodified ideal point is applied to execute a Pareto front decision.Finally,simulation tests based on IEEE33 and IEEE69 nodes are designed.The experimental results show thatMOPSO can achieve wider and more uniformPareto front distribution.In the IEEE33 node test system,power loss,and voltage deviation decreased by 52.23%,and 38.89%,respectively,while taking the economy into account.In the IEEE69 test system,the three indexes decreased by 19.67%,and 58.96%,respectively.展开更多
The pitch angle distributions of ions and electrons can be affected by various processes;thus,they can serve as an important indicator of the physical mechanisms driving the dynamics of space plasmas.From observations...The pitch angle distributions of ions and electrons can be affected by various processes;thus,they can serve as an important indicator of the physical mechanisms driving the dynamics of space plasmas.From observations from the Mars Ion and Neutral Particle Analyzer onboard the Tianwen-1 orbiter,we calculated the pitch angle distributions of protons in the Martian induced magnetosphere by using information from the magnetohydrodynamically simulated magnetic field,and we statistically analyzed the spatial occurrence pattern of different types of pitch angle distributions.Even though no symmetrical features were seen in the dataset,we found the dominance of the field-aligned distribution type over the energy range from 188 to 6232 eV.Maps of the occurrence rate showed the preferential presence of a trapped-like distribution at the lower altitudes of the surveyed nightside region.Although our results are more or less restricted by the adopted magnetic field,they indicate the complexity of the near-Mars proton pitch angle distributions and infer the possibility of wave–particle interactions in the Martian induced magnetosphere.展开更多
Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimiza...Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimization(PSO),but its native defect may result in the local optima trapped and convergence difficulty.In this paper,the genetic operations are introduced to the PSO,which makes the best hyperparameter combination scheme for specific network architecture be located easier.Spe-cifically,to prevent the troubles caused by the different data types and value scopes,a mixed coding method is used to ensure the effectiveness of particles.Moreover,the crossover and mutation opera-tions are added to the process of particles updating,to increase the diversity of particles and avoid local optima in searching.Verified with three benchmark datasets,MNIST,Fashion-MNIST,and CIFAR10,it is demonstrated that the proposed scheme can achieve accuracies of 99.58%,93.39%,and 78.96%,respectively,improving the accuracy by about 0.1%,0.5%,and 2%,respectively,compared with that of the PSO.展开更多
Deformable gel particles(DGPs) possess the capability of deep profile control and flooding. However, the deep migration behavior and plugging mechanism along their path remain unclear. Breakage, an inevitable phenomen...Deformable gel particles(DGPs) possess the capability of deep profile control and flooding. However, the deep migration behavior and plugging mechanism along their path remain unclear. Breakage, an inevitable phenomenon during particle migration, significantly impacts the deep plugging effect. Due to the complexity of the process, few studies have been conducted on this subject. In this paper, we conducted DGP flow experiments using a physical model of a multi-point sandpack under various injection rates and particle sizes. Particle size and concentration tests were performed at each measurement point to investigate the transportation behavior of particles in the deep part of the reservoir. The residual resistance coefficient and concentration changes along the porous media were combined to analyze the plugging performance of DGPs. Furthermore, the particle breakage along their path was revealed by analyzing the changes in particle size along the way. A mathematical model of breakage and concentration changes along the path was established. The results showed that the passage after breakage is a significant migration behavior of particles in porous media. The particles were reduced to less than half of their initial size at the front of the porous media. Breakage is an essential reason for the continuous decreases in particle concentration, size, and residual resistance coefficient. However, the particles can remain in porous media after breakage and play a significant role in deep plugging. Higher injection rates or larger particle sizes resulted in faster breakage along the injection direction, higher degrees of breakage, and faster decreases in residual resistance coefficient along the path. These conditions also led to a weaker deep plugging ability. Smaller particles were more evenly retained along the path, but more particles flowed out of the porous media, resulting in a poor deep plugging effect. The particle size is a function of particle size before injection, transport distance, and different injection parameters(injection rate or the diameter ratio of DGP to throat). Likewise, the particle concentration is a function of initial concentration, transport distance, and different injection parameters. These models can be utilized to optimize particle injection parameters, thereby achieving the goal of fine-tuning oil displacement.展开更多
基金The financial support from the National Pork Board,Des Moines,IA,USA,is greatly appreciated。
文摘Background Reduction of the particle size of corn increases energy digestibility and concentrations of digestible and metabolizable energy.Pelleting may also reduce particle size of grain,but it is not known if there are interactions between particle size reduction and pelleting.The objective of this experiment was to test the hypothesis that particle size reduction and pelleting,separately or in combination,increase N balance,apparent total tract digestibility(ATTD)of fiber and fat,and net energy(NE)in corn-soybean meal diets fed to group-housed pigs.Methods Six corn-soybean meal-based diets were used in a 3×2 factorial design with 3 particle sizes of corn(i.e.,700,500,or 300μm)and 2 diet forms(i.e.,meal or pelleted).Pigs were allowed ad libitum access to feed and water.Twenty-four castrated male pigs(initial weight:29.52 kg;standard diviation:1.40)were allotted to the 6 diets using a 6×6 Latin square design with 6 calorimeter chambers(i.e.,4 pigs/chamber)and 6 periods.Oxygen consumption and CO_(2)and CH_(4)productions were measured during fed and fasting states and fecal and urine samples were collected.Results Regardless of particle size of corn,the ATTD of gross energy(GE),N,and acid-hydrolyzed ether extract(AEE),and the concentration of NE were greater(P<0.05)in pelleted diets than in meal diets.Regardless of diet form,the ATTD of GE,N,and AEE,and the concentration of NE were increased(linear;P<0.05)by reducing the particle size of corn,but the increase was greater in meal diets than in pelleted diets(interaction;P<0.05).Conclusions Both pelleting and reduction of corn particle size increased nutrient digestibility and NE,but increases were greater in meal diets than in pelleted diets.
基金the National Natural Science Foundation of China(Grant No.42271436)the Shandong Provincial Natural Science Foundation,China(Grant Nos.ZR2021MD030,ZR2021QD148).
文摘The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.
基金Project supported by the Zhejiang Provincial Natural Science Foundation (Grant No.LQ20F020011)the Gansu Provincial Foundation for Distinguished Young Scholars (Grant No.23JRRA766)+1 种基金the National Natural Science Foundation of China (Grant No.62162040)the National Key Research and Development Program of China (Grant No.2020YFB1713600)。
文摘The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.
基金supported by Shandong Provincial Natural Science Foundation (ZR2023MB038)National Natural Science Foundation of China (21808232 and 21978143)Financial support from the Qingdao University of Science and Technology
文摘The homogeneous/particulate fluidization flow regime is particularly suitable for handling the various gas–solid contact processes encountered in the chemical and energy industry.This work aimed to extend such a regime of Geldart-A particles by exerting the axial uniform and steady magnetic field.Under the action of the magnetic field,the overall homogeneous fluidization regime of Geldart-A magnetizable particles became composed of two parts:inherent homogeneous fluidization and newly-created magnetic stabilization.Since the former remained almost unchanged whereas the latter became broader as the magnetic field intensity increased,the overall homogeneous fluidization regime could be extended remarkably.As for Geldart-A nonmagnetizable particles,certain amount of magnetizable particles had to be premixed to transmit the magnetic stabilization.Among others,the mere addition of magnetizable particles could broaden the homogeneous fluidization regime.The added content of magnetizable particles had an optimal value with smaller/lighter ones working better.The added magnetizable particles might raise the ratio between the interparticle force and the particle gravity.After the magnetic field was exerted,the homogeneous fluidization regime was further expanded due to the formation of magnetic stabilization flow regime.The more the added magnetizable particles,the better the magnetic performance and the broader the overall homogeneous fluidization regime.Smaller/lighter magnetizable particles were preferred to maximize the magnetic performance and extend the overall homogeneous fluidization regime.This phenomenon could be ascribed to that the added magnetizable particles themselves became more Geldart-A than-B type as their density or size decreased.
基金the Natural Science Foundation of China under Grant 52077027in part by the Liaoning Province Science and Technology Major Project No.2020JH1/10100020.
文摘In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.
基金supported by the National MCF Energy R&D Program of China(No.2018YFE0303100)National Natural Science Foundation of China(No.11975038)。
文摘The inward particle transport is associated with the formation of peaked density profiles,which contributes to improve the fusion rate and the realization of steady-state discharge.The active control of inward particle transport is considered as one of the most critical issues of magnetic confinement fusion.Recently,it is realized preliminarily by adding a biased endplate in the Peking University Plasma Test(PPT)device.The results reveal that the inward particle flux increases with the bias voltage of the endplate.It is also found that the profile of radial electric field(Er)shear is flattened by the increased bias voltage.Radial velocity fluctuations affect the inward particle more than density fluctuations,and the frequency of the dominant mode driving inward particle flux increases with the biased voltage applied to the endplate.The experimental results in the PPT device provide a method to actively control the inward particle flux using a biased endplate and enrich the understanding of the relationship between E_(r)×B shear and turbulence transport.
基金supported in part by the Ramanujan Fellowship from the Science and Engineering Research Board,Government of India(Grant No.RJF/2022/000115)。
文摘The breakage of brittle particulate materials into smaller particles under compressive or impact loads can be modelled as an instantiation of the population balance integro-differential equation.In this paper,the emerging computational science paradigm of physics-informed neural networks is studied for the first time for solving both linear and nonlinear variants of the governing dynamics.Unlike conventional methods,the proposed neural network provides rapid simulations of arbitrarily high resolution in particle size,predicting values on arbitrarily fine grids without the need for model retraining.The network is assigned a simple multi-head architecture tailored to uphold monotonicity of the modelled cumulative distribution function over particle sizes.The method is theoretically analyzed and validated against analytical results before being applied to real-world data of a batch grinding mill.The agreement between laboratory data and numerical simulation encourages the use of physics-informed neural nets for optimal planning and control of industrial comminution processes.
基金the National Natural Science Foundation of China(52177074).
文摘The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.
基金the National Natural Science Foundation of China(No.42127807)Natural Science Foundation of Sichuan Province(Nos.23NSFSCC0116 and 2022NSFSC12333)the Nuclear Energy Development Project(No.[2021]-88).
文摘In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.
基金supported by the Swiss National Science Foundation(Grant No.189882)the National Natural Science Foundation of China(Grant No.41961134032)support provided by the New Investigator Award grant from the UK Engineering and Physical Sciences Research Council(Grant No.EP/V012169/1).
文摘In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a generalised Hellinger-Reissner(HR)variational principle,creating an implicit PFEM formulation.To mitigate the volumetric locking issue in low-order elements,we employ a node-based strain smoothing technique.By discretising field variables at the centre of smoothing cells,we achieve nodal integration over cells,eliminating the need for sophisticated mapping operations after re-meshing in the PFEM.We express the discretised governing equations as a min-max optimisation problem,which is further reformulated as a standard second-order cone programming(SOCP)problem.Stresses,pore water pressure,and displacements are simultaneously determined using the advanced primal-dual interior point method.Consequently,our numerical model offers improved accuracy for stresses and pore water pressure compared to the displacement-based PFEM formulation.Numerical experiments demonstrate that the N-PFEM efficiently captures both transient and long-term hydro-mechanical behaviour of saturated soils with high accuracy,obviating the need for stabilisation or regularisation techniques commonly employed in other nodal integration-based PFEM approaches.This work holds significant implications for the development of robust and accurate numerical tools for studying saturated soil dynamics.
基金supported by the National Natural Science Foundation of China(Nos.U22A20166,52104141,12172230 and U2013603)the Department of Science and Technology of Guangdong Province(No.2019ZT08G315)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515012654).
文摘Interparticle adhesion force has a controlling effect on the physical and mechanical properties of planetary regolith and rocks.The current research on the adhesion force of planetary regolith and rock particles has been primarily based on the assumption of smooth spherical particles to calculate the intergranular adhesion force;this approach lacks consideration for the adhesion force between irregular shaped particles.In our study,an innovative approach was established to directly measure the adhesion force between the arbitrary irregular shaped particles;the probe was modified using simulated lunar soil particles that were a typical representation of planetary regolith.The experimental results showed that for irregular shaped mineral particles,the particle size and mineral composition had no significant influence on the interparticle adhesion force;however,the complex morphology of the contact surface predominantly controlled the adhesion force.As the contact surface roughness increased,the adhesion force gradually decreased,and the rate of decrease gradually slowed;these results were consistent with the change trend predicted via the theoretical models of quantum electrodynamics.Moreover,a theoretical model to predict the adhesion force between the irregular shaped particles was constructed based on Rabinovich’s theory,and the prediction results were correlated with the experimental measurements.
基金supported by the National Natural Science Foundation of China (61502522)Hubei Provincial Natural Science Foundation(2019CFC897)。
文摘To meet the requirements of safety, concealment, and timeliness of trajectory planning during the unmanned aerial vehicle(UAV) penetration process, a three-dimensional path planning algorithm is proposed based on improved holonic particle swarm optimization(IHPSO). Firstly, the requirements of terrain threat, radar detection, and penetration time in the process of UAV penetration are quantified. Regarding radar threats, a radar echo analysis method based on radar cross section(RCS)and the spatial situation is proposed to quantify the concealment of UAV penetration. Then the structure-particle swarm optimization(PSO) algorithm is improved from three aspects.First, the conversion ability of the search strategy is enhanced by using the system clustering method and the information entropy grouping strategy instead of random grouping and constructing the state switching conditions based on the fitness function.Second, the unclear setting of iteration numbers is addressed by using particle spacing to create the termination condition of the algorithm. Finally, the trajectory is optimized to meet the intended requirements by building a predictive control model and using the IHPSO for simulation verification. Numerical examples show the superiority of the proposed method over the existing PSO methods.
基金supported in part by the National Key Research and Development Program of China under Grant 2019YFB2102102in part by the National Natural Science Foundations of China under Grant 62176094 and Grant 61873097+2 种基金in part by the Key‐Area Research and Development of Guangdong Province under Grant 2020B010166002in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003in part by the Guangdong‐Hong Kong Joint Innovation Platform under Grant 2018B050502006.
文摘Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.
基金The authors extended their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Large Groups Project under grant number RGP.2/132/43。
文摘At present Bayesian Networks(BN)are being used widely for demonstrating uncertain knowledge in many disciplines,including biology,computer science,risk analysis,service quality analysis,and business.But they suffer from the problem that when the nodes and edges increase,the structure learning difficulty increases and algorithms become inefficient.To solve this problem,heuristic optimization algorithms are used,which tend to find a near-optimal answer rather than an exact one,with particle swarm optimization(PSO)being one of them.PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds(how they search for food).PSO is employed widely because it is easier to code,converges quickly,and can be parallelized easily.We use a recently proposed version of PSO called generalized particle swarm optimization(GEPSO)to learn bayesian network structure.We construct an initial directed acyclic graph(DAG)by using the max-min parent’s children(MMPC)algorithm and cross relative average entropy.ThisDAGis used to create a population for theGEPSO optimization procedure.Moreover,we propose a velocity update procedure to increase the efficiency of the algorithmic search process.Results of the experiments show that as the complexity of the dataset increases,our algorithm Bayesian network generalized particle swarm optimization(BN-GEPSO)outperforms the PSO algorithm in terms of the Bayesian information criterion(BIC)score.
文摘A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization.The primary aim of MANETs is to extendflexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing system.An Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/violations.An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks,sending out alerts whenever it detects malicious nodes.The impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research article.The Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET environment.MANET system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node trustworthiness.Furthermore,KF is used to eliminate synchronisation errors that arise during the sending and receiving data.In order to provide an energy-efficient solution and to minimize network traffic,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in nodes.According to the researchfindings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.
文摘Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to the size of the memory,less storage capacity is experienced due to the restricted size of the respective node.In this proposed work,we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization(PSO).PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space.PSO is integrated with a Reduced Coverage Set,to obtain an optimal path with only high-power transmitting nodes.Energy Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution,to cover the whole network.The proposed algorithm has experimented with a different number of nodes.Comparison has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18%~40%,thereby increasing the network lifetime.
基金The authors gratefully acknowledge the support of the Enhancement Strategy of Multi-Type Energy Integration of Active Distribution Network(YNKJXM20220113).
文摘In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distributed generation(DG)based on renewable energy is critical for active distribution network operation enhancement.To comprehensively analyze the accessing impact of DG in distribution networks from various parts,this paper establishes an optimal DG location and sizing planning model based on active power losses,voltage profile,pollution emissions,and the economics of DG costs as well as meteorological conditions.Subsequently,multiobjective particle swarm optimization(MOPSO)is applied to obtain the optimal Pareto front.Besides,for the sake of avoiding the influence of the subjective setting of the weight coefficient,the decisionmethod based on amodified ideal point is applied to execute a Pareto front decision.Finally,simulation tests based on IEEE33 and IEEE69 nodes are designed.The experimental results show thatMOPSO can achieve wider and more uniformPareto front distribution.In the IEEE33 node test system,power loss,and voltage deviation decreased by 52.23%,and 38.89%,respectively,while taking the economy into account.In the IEEE69 test system,the three indexes decreased by 19.67%,and 58.96%,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.42241143)the pre-research projects on Civil Aerospace Technologies(No.D020104)funded by the China National Space Administration.
文摘The pitch angle distributions of ions and electrons can be affected by various processes;thus,they can serve as an important indicator of the physical mechanisms driving the dynamics of space plasmas.From observations from the Mars Ion and Neutral Particle Analyzer onboard the Tianwen-1 orbiter,we calculated the pitch angle distributions of protons in the Martian induced magnetosphere by using information from the magnetohydrodynamically simulated magnetic field,and we statistically analyzed the spatial occurrence pattern of different types of pitch angle distributions.Even though no symmetrical features were seen in the dataset,we found the dominance of the field-aligned distribution type over the energy range from 188 to 6232 eV.Maps of the occurrence rate showed the preferential presence of a trapped-like distribution at the lower altitudes of the surveyed nightside region.Although our results are more or less restricted by the adopted magnetic field,they indicate the complexity of the near-Mars proton pitch angle distributions and infer the possibility of wave–particle interactions in the Martian induced magnetosphere.
基金the National Key Research and Development Program of China(No.2022ZD0119003)the National Natural Science Foundation of China(No.61834005).
文摘Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimization(PSO),but its native defect may result in the local optima trapped and convergence difficulty.In this paper,the genetic operations are introduced to the PSO,which makes the best hyperparameter combination scheme for specific network architecture be located easier.Spe-cifically,to prevent the troubles caused by the different data types and value scopes,a mixed coding method is used to ensure the effectiveness of particles.Moreover,the crossover and mutation opera-tions are added to the process of particles updating,to increase the diversity of particles and avoid local optima in searching.Verified with three benchmark datasets,MNIST,Fashion-MNIST,and CIFAR10,it is demonstrated that the proposed scheme can achieve accuracies of 99.58%,93.39%,and 78.96%,respectively,improving the accuracy by about 0.1%,0.5%,and 2%,respectively,compared with that of the PSO.
基金supported by the Major National Science and Technology Project(No.2016ZX05054011)。
文摘Deformable gel particles(DGPs) possess the capability of deep profile control and flooding. However, the deep migration behavior and plugging mechanism along their path remain unclear. Breakage, an inevitable phenomenon during particle migration, significantly impacts the deep plugging effect. Due to the complexity of the process, few studies have been conducted on this subject. In this paper, we conducted DGP flow experiments using a physical model of a multi-point sandpack under various injection rates and particle sizes. Particle size and concentration tests were performed at each measurement point to investigate the transportation behavior of particles in the deep part of the reservoir. The residual resistance coefficient and concentration changes along the porous media were combined to analyze the plugging performance of DGPs. Furthermore, the particle breakage along their path was revealed by analyzing the changes in particle size along the way. A mathematical model of breakage and concentration changes along the path was established. The results showed that the passage after breakage is a significant migration behavior of particles in porous media. The particles were reduced to less than half of their initial size at the front of the porous media. Breakage is an essential reason for the continuous decreases in particle concentration, size, and residual resistance coefficient. However, the particles can remain in porous media after breakage and play a significant role in deep plugging. Higher injection rates or larger particle sizes resulted in faster breakage along the injection direction, higher degrees of breakage, and faster decreases in residual resistance coefficient along the path. These conditions also led to a weaker deep plugging ability. Smaller particles were more evenly retained along the path, but more particles flowed out of the porous media, resulting in a poor deep plugging effect. The particle size is a function of particle size before injection, transport distance, and different injection parameters(injection rate or the diameter ratio of DGP to throat). Likewise, the particle concentration is a function of initial concentration, transport distance, and different injection parameters. These models can be utilized to optimize particle injection parameters, thereby achieving the goal of fine-tuning oil displacement.