Collective Motion Of Particles Research Articles

Collective motion of active particles exhibiting non-reciprocal orientational interactions

We present a Brownian dynamics study of a 2D bath of active particles interacting among each other through usual steric interactions and, additionally, via non-reciprocal avoidant orientational interactions. We motivate them by the fact that the two flagella of the alga Chlamydomonas interact sterically with nearby surfaces such that a torque acts on the alga. As expected, in most cases such interactions disrupt the motility-induced particle clustering in active baths. Surprisingly, however, we find that the active particles can self-organize into collectively moving flocks if the range of non-reciprocal interactions is close to that of steric interactions. We observe that the flocking motion can manifest itself through a variety of structural forms, spanning from single dense bands to multiple moderately-dense stripes, which are highly dynamic. The flocking order parameter is found to be only weakly dependent on the underlying flock structure. Together with the variance of the local-density distribution, one can clearly group the flocking motion into the two separate band and dynamic-stripes states.

A method to predict particle collision speeds in fluidized beds

The target of this method is gas–solid fluidized beds, in which the particles progressively become coated with liquid or generally become sticky for any reason. Previous studies have shown that the restitution coefficient of wet particle collisions depends on the speed at which the particles collide. Moreover, below a critical collision speed, the particles fail to rebound at all. Therefore, predicting particle collision speeds is important. Current methods to predict collision speeds are computationally expensive. The present method provides an analytical expression for particle collision speed, making it convenient for evaluating the potential for agglomeration. Moreover, it provides a basis for understanding the speed of particle motion in fluidized beds—usually only accessible through time-consuming numerical simulations—by anticipating the outcome of what is largely a repetitive computation of particle–particle collision and re-acceleration by the gas flow. The method combines the mean free time between inelastic particle collisions (with provision for collective particle motion), which cause them to lose kinetic energy, and the drag force by the turbulent gas flow on the particles, which accelerates them prior to their next collision. To validate the method, the results are compared to corresponding computational fluid dynamics (CFD) - discrete element method (DEM) calculations.

In Silico Studies of Active Probe Dynamics in Crowded Media.

Active systems are made of agents, each of which takes energy from the environment and converts it to directed motion. Therefore, by construction, these systems function out of equilibrium and cannot be described using equilibrium statistical mechanics. Though the most studied aspect has been the collective motion of active particles, the motion at the individual particle level in crowded media is also of prime importance. Examples include the motion of bacteria in hydrogels, single cell migration as a way to search for food or escape from toxic agents, and synthetic active agents transporting through soft crowded media. This review presents an overview of our understanding of single active probe dynamics in crowded media from computer simulations. The active probe is a Janus or a dumbbell-shaped particle, and the medium is made of crowders that are either sticky or repulsive to the probe and could be frozen or mobile. The density and the topology of the crowders also play an important role. We hope our in silico studies will help to elucidate the mechanism of activity-driven transport in crowded media in general and design nanomachines for targeted delivery.

Nonvanishing optimal noise in cellular automaton model of self-propelled particles

A minimal cellular automaton model is introduced to describe the collective motion of self-propelled particles on two-dimensional square lattice. The model features discretization of directional and positional spaces and single-particle occupation on one lattice site. Contrary to the Vicsek model and its variants, our model exhibits the nonvanishing optimal noise. When the particle density increases, the collective motion is promoted with optimal noise strength and reduced with noise strength out of optimal region. In addition, when the square lattice undergoes edge percolation process, no abrupt change of alignment behaviors is observed at the critical point of percolation.

Collective motion of chiral particles based on the Vicsek model

The research of chiral particles is of great significance to understanding the collective behavior of biomolecules. In this paper, we research the collective motion of chiral particles based on the Vicsek model using computer numerical simulations. When there is no noise, it is found that there is a critical coupling density ρc. When the coupling density of the particle is smaller than the critical coupling density, the order parameter of the particle will decrease with the increase of the chirality. When the coupling density is greater than the critical coupling density, the order parameter does not change with the chirality. In addition, the critical coupling density decreases as the particle number N and decoupled parameter L/r increase, but first decreases and then increases as the decoupled parameter v/r increases. Further, when there is noise, the order parameter of the particle with small coupling density or small noise amplitude will decrease with the increase of chirality, whereas the order parameter with large coupling density or large noise amplitude will not change. Our results improve the collective motion properties of chiral self-propelled particles. Besides, our work may inspire new strategies for manipulating self-propelled particles with different chirality to maintain a highly ordered state.

Focusing on transient chaos

Recent advances in the field of complex, transiently chaotic dynamics are reviewed, based on the results published in the focus issue of J. Phys. Complex. on this topic. One group of achievements concerns network dynamics where transient features are intimately related to the degree and stability of synchronization, as well as to the network topology. A plethora of various applications of transient chaos are described, ranging from the collective motion of active particles, through the operation of power grids, cardiac arrhythmias, and magnetohydrodynamical dynamos, to the use of machine learning to predict time evolutions. Nontraditional forms of transient chaos are also explored, such as the temporal change of the chaoticity in the transients (called doubly transient chaos), as well as transients in systems subjected to parameter drift, the paradigm of which is climate change.

Collective topological active particles: Non-ergodic superdiffusion and ageing in complex environments

We investigate the collective motion of active particles in complex environments. Two types of topological neighborhood, k-nearest neighbors and Voronoi tessellation, are considered, for each active particle interacts with other active particles or obstacles. By virtue of numerical simulations, we demonstrate the superdiffusion, ergodicity breaking and ageing in this system. The misalignment between the active particles and obstacles gives rise to the anisotropy of motion of topological active particles, and is the main mechanism of observed behaviors. Our work merges the non-ergodicity and superdiffusion and sheds light on the topological interaction of active particles in complex environments.

Identification of collective particle motion in a rotating drum using a graph community detection algorithm

The discrete element method (DEM) is the method of choice in many cases of simulation and analysis of processes in granular matter, providing the data at the level of individual particles. In most applications and experiments, however, the bulk behavior is of interest; therefore, larger scale structures must be identified from the DEM simulation data. We present the method for detection of particle groups involved in collective motion based on network analysis. Knowing the positions and velocities of individual particles, a “velocity similarity graph” is built, where the graph vertices represent the particles. The vertex pairs are connected by the edge if the distance between the respective particles is small enough. The edge weight is calculated to be inversely proportional to the difference in the respective particle velocities, that is, the vertex pairs representing nearby particles having similar velocities are connected by edges of larger weight. If a group of particles moves in a coordinated matter, the particles in this group will have similar velocities; therefore, the corresponding vertices in the graph will be connected by edges of larger weight in the representing graph. Having produced the velocity similarity graph, identification of particle groups becomes equivalent to the problem of “community detection” in graph analysis. The algorithms and techniques developed for community detection in graphs can be thereby applied for identification of particle groups involved in coordinated motion in granular matter. We illustrate this approach by an example of granular media filled in a rotating cylinder.

Modeling the influence of noise and interaction strength on the collective motion of interactive particles

Modeling the influence of noise and interaction strength on the collective motion of interactive particles

Enhancing directed collective motion of self-propelled particles in confined channel

The collective transport of the self-propelled rods (SPRs) is studied by dissipative particle dynamics simulations. Two types of channels (channel I and channel II) are taken into account for various rod concentrations. It is found that in channel I—the asymmetric corrugated channel with periodically varying width, some SPRs are trapped at the corners and form the hedgehog clusters. Other SPRs aggregate at the bottleneck and lead to a traffic jam. Consequently, channel I is inefficient for the directional SPR transport in the case of finite concentration. To achieve efficient collective particle transport, channel II—the channel with constant width and arrays of asymmetric obstacles within it, which can avoid the traffic clogging and hedgehog aggregate is suggested. It is found that the swimmer-obstacle interaction gives rise to the directional motion, the spacing between obstacles can avoid the formation of the hedgehog clusters. The high-efficiency directional collective motion of the SPRs is acquired in channel II. Overall, our simulation study offers an efficient approach for directional collective motion of SPRs.

Anomalous transport on a corrugated ratchet potential

We present an extensive study the motion of particles in a rocking ratchet potential corrugated by quenched correlated disorder. The spatial disorder slows down the collective motion of particles. Anomalous transport in its both forms, subdiffusion and superdiffusion has been observed at long times. We provide a qualitative explanation for the origin of these anomalies. These behaviors are a direct consequence of the interplay between the ratchet potential roughness and the temperature of the system. In the same way as Khoury et al [1], we find a relation between the mean velocity of the particles and the different kinds of diffusion at long time.

Collective motion of polar active particles on a sphere* *Project supported in part by the National Natural Science Foundation of China (Grant Nos. 12075090 and 12005066), the Science and Technology Program of Guangzhou (Grant No. 2019050001), the Natural Science Foundation of Guangdong Province, China (Grant No. 2017A030313029), and the Major Basic Research Project of Guangdong Province, China (Grant No. 2017KZDXM024).

Collective motion of active particles with polar alignment is investigated on a sphere. We discussed the factors that affect particle swarm motion and define an order parameter that can show the degree of particle swarm motion. In the model, we added a polar alignment strength, along with Gaussian curvature, affecting particles swarm motion. We find that when the force exceeds a certain limit, the order parameter will decrease with the increase of the force. Combined with our definition of order parameter and observation of the model, the reason is that particles begin to move side by side under the influence of polar forces. In addition, the effects of velocity, rotational diffusion coefficient, and packing fraction on particle swarm motion are discussed. It is found that the rotational diffusion coefficient and the packing fraction have a great influence on the clustering motion of particles, while the velocity has little influence on the clustering motion of particles.

Polymer Glass Formation: Role of Activation Free Energy, Configurational Entropy, and Collective Motion

We provide a perspective on polymer glass formation, with an emphasis on models in which the fluid entropy and collective particle motion dominate the theoretical description and data analysis. The entropy theory of glass formation has its origins in experimental observations relating to correlations between the fluid entropy and liquid dynamics going back nearly a century ago, and it has entered a new phase in recent years. We first discuss the dynamics of liquids in the high-temperature Arrhenius regime, where transition state theory is formally applicable. We then summarize the evolution of the entropy theory from a qualitative framework for organizing and interpreting temperature-dependent viscosity data by Kauzmann to the formulation of a hypothetical “ideal thermodynamic glass transition” by Gibbs and DiMarzio, followed by seminal measurements linking entropy and relaxation by Bestul and Chang and the Adam–Gibbs (AG) model of glass formation rationalizing the observations of Bestul and Chang. These developments laid the groundwork for the generalized entropy theory (GET), which merges an improved lattice model of polymer thermodynamics accounting for molecular structural details and enabling the analytic calculation of the configurational entropy with the AG model, giving rise to a highly predictive model of the segmental structural relaxation time of polymeric glass-forming liquids. The development of the GET has occurred in parallel with the string model of glass formation in which concrete realizations of the cooperatively rearranging regions are identified and quantified for a wide range of polymeric and other glass-forming materials. The string model has shown that many of the assumptions of AG are well supported by simulations, while others are certainly not, giving rise to an entropy theory of glass formation that is largely in accord with the GET. As the GET and string models continue to be refined, these models progressively grow into a more unified framework, and this Perspective reviews the present status of development of this promising approach to the dynamics of polymeric glass-forming liquids.

Theoretical Evidence for Wave Nature of Micro Particle and New Theory of Its Collective Motion in Material

Since a material is composed of micro particles, investigating behavior of those particles is essentially dominant for materials science. The diffusivity of diffusion equation is relevant to not only a collective motion of micro particles but also a motion of single particle. An elementary process of diffusion was thus theoretically investigated in a local space and time. As a result, the investigation concluded that the wave nature of micro particle results from denying the mathematical density theorem of a real time in the Newton mechanics. In other words, the basic theory of quantum mechanics is established in accordance with the cause-and-effect relationship in the Newton mechanics, for the first time, regardless of the de Broglie hypothesis. In relation to the collective motion of micro particles, the new diffusion theory was also reasonably established using the universal expression of diffusivity obtained here. In the present paper, the new findings indispensable for the fundamental knowledge in physics are thus systematically discussed in accordance with the theoretical frame in physics.

Correction: Collective motion of chiral Brownian particles controlled by a circularly-polarized laser beam.

Correction for 'Collective motion of chiral Brownian particles controlled by a circularly-polarized laser beam' by Raúl Josué Hernández et al., Soft Matter, 2020, 16, 7704-7714, DOI: .

Langevin Dynamic Simulation of self-propelled particles in two-dimensional systems

Collective motion of self-propelled particles is one of basic phenomenon observed in large spectra of biological system behavior due to the correlated process evolution in space and time. In this manuscript, we study numerically the kinetic properties and the correlation process in complex systems evolves out equilibrium phase by employing the Langevin dynamics. In this model, we have adopted one zone of orientation where the system evolves spontaneously in presence of quenched stochastic noise. The results show that the system evolves to its equilibrium phase by reaching one orientation. Hence, this evolution is characterized by a correlation process increasing in time but with decelerate profile. However the obtained profile of the correlation function per time unity shows that the collective motion in complex system, can be characterized by a characteristic time when the system change the acceleration of the correlation process. Our result shows that this characteristic time decreases exponentially with the quenched noise. In the additional crossover time at when the system reaches its equilibrium phase, scales with quenched noise as power law. This result is more consisting with the one of Vicsek model

Collective motion of self-propelled particles with complex noise environments

Collective motion of self-propelled particles with complex noise environments is investigated via simulations based on the Vicsek model. In our model, self-propelled particles move in a square divided into one part with noise and the other without noise. Via simulations, it is found that the proportion of noise region p has an important impact on the collective motion of the system. Specifically, there is a transition at the critical pc. When p<pc, all particles of the system can reach the global synchronization; on the contrary, when p>pc, the order parameter of synchronization rapidly decreases. More interestingly, the value of pc decreases as the noise amplitude η increases. Simultaneously, when p<pc, the proportion of the number of particles in the noise region λ approximates zero; conversely, when p>pc, the value of λ increases dramatically. Furthermore, pc gradually increases as the number of particles increases. Our results provide new sights into the study of collective motion in nature.

Generation of megatesla magnetic fields by intense-laser-driven microtube implosions

A microtube implosion driven by ultraintense laser pulses is used to produce ultrahigh magnetic fields. Due to the laser-produced hot electrons with energies of mega-electron volts, cold ions in the inner wall surface implode towards the central axis. By pre-seeding uniform magnetic fields on the kilotesla order, the Lorenz force induces the Larmor gyromotion of the imploding ions and electrons. Due to the resultant collective motion of relativistic charged particles around the central axis, strong spin current densities of sim peta-ampere/hbox {cm}^{2} are produced with a few tens of nm size, generating megatesla-order magnetic fields. The underlying physics and important scaling are revealed by particle simulations and a simple analytical model. The concept holds promise to open new frontiers in many branches of fundamental physics and applications in terms of ultrahigh magnetic fields.

Electric field measurement in electric-field modified flames

The application of an electric field to a flame modifies the flame characteristics due to the collective motion of charged particles generated within the flame. The applied field mobilises electrons and ions depending on their polarity, leading to localised regions of space charge with sufficient density to either locally amplify or shield the applied electric field. A fundamental understanding of the local electric field is essential for both electrically and plasma-assisted combustion, because it determines electric body force as well as electron temperature. In this work, we propose a method, which is independent of gas composition and temperature, to measure the local electric field using electric field induced second harmonic generation (E-FISH). We successfully apply this method to a laminar nonpremixed counterflow flame by applying modulated direct current (DC) electric field with a microsecond duration zero volt pulse. Electric potential and space charge distribution are also deduced from the measured electric field. Furthermore, we show the importance of electron attachment to O2 forming O2− by changing the polarity of the applied DC electric field. When the anode is in the oxygen stream, a region of negative charge is obtained near the anode, whereas, when the anode is in the fuel stream, no region of negative charge is found. We also find the qualitative trends of the measured electric fields reasonably agree with previously reported one-dimensional modelling results. The limitations of the methodology are addressed, and we show that the ability to modulate the DC voltage on sub-microsecond timescales is required for accurate quantitative measurements.

Linked vehicle model: A simple car-following model for automated vehicles

The expectation from automated vehicles has been increasing in recent years. Potential advantages for automated vehicles include driving safety, traffic efficiency, and positive environmental outcomes. To evaluate the extent to which automated vehicles are advantageous against conventional vehicles, this paper presents a simple car-following model, named the linked vehicle model, which reflects the capabilities of automated vehicles and the interaction between adjacent automated vehicles. The idea behind linked vehicle model is inspired by the collective motion of self-propelled particles in nature and the study of vehicle platooning from the Safe Road Trains for the Environment project. Moreover, the model is integrated into a traffic simulator in order to evaluate the performance of automated vehicles in an ideal circumstance. Comparisons with other representative car-following models are also provided, and they show that linked vehicle model achieves both optimal microscopic and macroscopic performance. Simulated results also show that linked vehicle model attains the best fuel economy and the lowest pollutant emissions.