Particles In Domain Research Articles

Collective dynamic length increases monotonically in pinned and unpinned glass forming systems.

The Random First-Order Transition (RFOT) theory predicts that transport proceeds by the cooperative movement of particles in domains, whose sizes increase as a liquid is compressed above a characteristic volume fraction, ϕd. The rounded dynamical transition around ϕd, which signals a crossover to activated transport, is accompanied by a growing correlation length that is predicted to diverge at the thermodynamic glass transition density (>ϕd). Simulations and imaging experiments probed the single particle dynamics of mobile particles in response to pinning all the particles in a semi-infinite space or randomly pinning (RP) a fraction of particles in a liquid at equilibrium. The extracted dynamic length increases non-monotonically with a peak around ϕd, which not only depends on the pinning method but is also different from ϕd of the actual liquid. This finding is at variance with the results obtained using the small wavelength limit of a four-point structure factor for unpinned systems. To obtain a consistent picture of the growth of the dynamic length, one that is impervious to the use of RP, we introduce a multiparticle structure factor, Smpc(q,t), that probes collective dynamics. The collective dynamical length, calculated from the small wave vector limit of Smpc(q,t), increases monotonically as a function of the volume fraction in a glass-forming binary mixture of charged colloidal particles in both unpinned and pinned systems. This prediction, which also holds in the presence of added monovalent salt, may be validated using imaging experiments.

Controlled transport of fluid particles by microrotors in a Stokes flow using linear transfer operators

The manipulation of a collection of fluid particles in a low Reynolds number environment has several important applications. As we demonstrate in this paper, this manipulation problem is related to the scientific question of how fluid flow structures direct Lagrangian transport. We investigate this problem of directing the transport by manipulating the flow, specifically in the Stokes flow context, by controlling the strengths of two rotors fixed in space. We demonstrate a novel dynamical systems approach for this problem and apply this method to several scenarios of Stokes flow in unbounded and bounded domains. Furthermore, we show that the time-varying flow field produced by the optimal control can be understood in terms of dynamical structures such as coherent sets that define Lagrangian transport. We model the time evolution of the fluid particle density using finite-dimensional approximations of the Liouville operators for the microrotor flow fields. Using these operators, the particle transport problem is framed as an optimal control problem, which we solve numerically. This framework is then applied to the problem of transporting a blob of fluid particles in domains with different boundary conditions: free space, near to a plane wall, in a circular confinement, and the transport of two distributions of particles to a common target. These examples demonstrate the effectiveness of the proposed framework and also shed light on the effects of boundaries on the ability to achieve a desired fluid transport using a rotor-driven flow.

Fast simulation of particulate suspensions enabled by graph neural network

Predicting the dynamic behaviors of particles in suspension subject to hydrodynamic interaction (HI) and external drive can be critical for many applications. By harvesting advanced deep learning techniques, the present work introduces a new framework, hydrodynamic interaction graph neural network (HIGNN), for inferring and predicting the particles’ dynamics in Stokes suspensions. It overcomes the limitations of traditional approaches in computational efficiency, accuracy, and/or transferability. In particular, by uniting the data structure represented by a graph and the neural networks with learnable parameters, the HIGNN constructs surrogate modeling for the mobility tensor of particles which is the key to predicting the dynamics of particles subject to HI and external forces. To account for the many-body nature of HI, we generalize the state-of-the-art GNN by introducing higher-order connectivity into the graph and the corresponding convolutional operation. For training the HIGNN, we only need the data for a small number of particles in the domain of interest, and hence the training cost can be maintained low. Once constructed, the HIGNN permits fast predictions of the particles’ velocities and is transferable to suspensions of different numbers/concentrations of particles in the same domain and to any external forcing. It has the ability to accurately capture both the long-range HI and short-range lubrication effects. We demonstrate the accuracy, efficiency, and transferability of the proposed HIGNN framework in a variety of systems. The requirement on computing resource is minimum: most simulations only require a desktop with one GPU; the simulations for a large suspension of 100,000 particles call for up to 6 GPUs.

Extreme-scale particle-based simulations on advanced HPC platforms

We overview the current status and future development directions of our framework for developing particle simulator (FDPS). Many of particle-based simulation codes share the same characteristic that the most time-consuming part of the simulation is the calculation of the interactions between particles, and a large fraction of programming effort is spent for procedures to make the force calculation efficient, such as the decomposition of computational domain, exchange of particles between domains, exchange of information necessary to calculate the interaction to particles in different domains, and efficient neighbor search. The basic idea of FDPS is to provide generic and high-performance library for these procedures. Using these procedures, researchers or application programmers in various fields can write their programs without taking care of parallelization and performance tuning. In order to make FDPS useful on advanced HPC platforms at present and in (near) future, we investigated its performance on several modern platforms and learned what can be the bottleneck. In this paper we summarize what we learned.

Development of the New Analytic Model for Sand Deposition Particles Downstream of a Fence

Movement of sand particles is a complicated phenomenon that occurs in nature. In this paper, the main goal is to provide an analytic model for the deposition profile of sand particles downstream of a fence. The analytic model was derived with respect to governing equations and shear flows for upstream and downstream regions. In this approach, we obtain a new expression for the downstream velocity of the fence, which allows for the determination of potential areas of deposition particles by assuming a log-normal distribution profile. A discrete-phase flow (DPM) was used to inject particles in the simulation domain. The DPM gives capabilities to capture spatiotemporal velocities components, as we can define the probability of deposition particles in the downstream of the fence. The proposed model was validated with a numerical model and experimental results. The comparison with field data and numerical results shows that the deposition profile is in acceptable agreement. With some assumptions and modifications about the properties of particles, the results of this research can be extended to snow accumulation downstream of a fence.

Research on the Effect of Particle Distribution on the Crack Propagation in Shaped Energy Blasting Based on the Smoothed Particle Hydrodynamics

Conventional smoothed particle hydrodynamics (SPH) methods suffer from disadvantages, such as difficult initial particle configuration, uneven distribution of generated particles, and low computational efficiency when applied to numerical simulation of shaped charge blasting. In this research, to overcome these problems, a modified SPH method that generates the particle configuration through self-adaptive optimization is developed by the combined application of MATLAB and LS-DYNA. The results presented in this paper demonstrate that the modified configuration method solves the problem of uneven distribution of particles in complex geometry domains by providing a more uniform smoothed particle distribution than the conventional SPH method. Furthermore, the results from the application of these two methods to the bidirectional-shaped charge blasting problem reveal that the defects in the particle configuration in the conventional SPH method lead to the development of main cracks in both the shaped and the unshaped directions. However, with the self-adaptive optimization method, the main cracks develop only in the shaped direction. In addition, the equivalent stress difference between the shaped and unshaped directions, 0.7 ms after detonation, is 120 MPa with the modified method. This is 85 MPa more than that with the conventional method.

The SHiP physics program at CERN

The discovery of the Higgs boson has fully confirmed the Standard Model of particles and fields. Nevertheless, there are still fundamental phenomena, like the existence of dark matter, the neutrino masses and the baryon asymmetry of the Universe, which deserve an explanation that could come from the discovery of new particles. The SHiP experiment at CERN is proposed to search for very weakly coupled particles in the few GeV mass domain where the existence of such particles is largely unexplored. A beam dump facility using high intensity 400 GeV protons is a copious source of such unknown particles in the GeV mass range. The beam dump is also a very intense source of neutrinos and, in particular, of tau neutrinos, the less known particle in the Standard Model. We report the physics potential of such an experiment. An ancillary measurement of the charm cross-section was carried out in July 2018 and the data are under analysis and we report preliminary results. Moreover, a prototype of the neutrino detector is being designed to possibly take data at the LHC in its Run3 of operation. We describe the proposed detector and the physics case.

Size dependent buckling analysis of nano sandwich beams by two schemes

Within the framework of Timoshenko beam theory, the buckling of nano sandwich beams is developed. The material properties are assumed to vary arbitrarily in both axial and thickness directions. These types of beams are referred to as bi-directional functionally graded (BDFG) beams. Two types of nano sandwich beams with different material distribution patterns and immovable supports are considered. Since the size effects play a significant role in mechanical behavior of nanostructures, the small-scale effects are captured by Eringen’s nonlocal theory of elasticity. The governing equations are derived using the variational formulation. Symmetric smoothed particle hydrodynamics (SSPH) and the Galerkin method are adopted as numerical solution approaches. As a truly meshless method, the convergence of the SSPH technique mainly depends on the smoothing length value and distribution of particles in the compact support domain of the kernel function. The Revised Super Gauss Function is used as the kernel function and an optimum value for the smoothing length that bears the fastest convergence rate is obtained. The solution methods are verified through benchmark problems found in the literature. Numerical and illustrative results show that various parameters, including the aspect ratio, nonlocal parameter, gradient indexes, and cross-sectional types have significant effects on the buckling responses of BDFG nano sandwich beams.

Tricolor Technique for Visualization of Spatial Variations of Polydisperse Dust in Gas-Dust Flows

The aim of this work is to construct an algorithm for visualizing a polydisperse phase of solid particles (dust) in an inhomogeneous flow of a two-phase gas-dust mixture that would allow us to see, within one plot, the degree of polydispersity of the dust phase and the difference in the spatial distributions of individual fractions of dust particles in the computational domain. The developed technique allows us to reproduce concentrations from one to three fractions of dust particles in each cell in the computational domain. Each of the three fractions of dust particles is mapped to one of the main channels of the RGB palette. The intensity of the color shade is set to be proportional to the relative concentration of dust particles in this fraction. The final image for a polydisperse mixture is obtained by adding images in each of the three color channels. To visualize the degree of polydispersity, we propose depicting the spatial distribution of the entropy of the dust mixture. The definition of the entropy of a mixture is generalized to take into account the states of a mixture with zero number of particles in the mixture. They correspond to dust-free sections of the computational domain (voids). The proposed method for visualizing the polydispersity of a mixture of particles is demonstrated using the example of dynamic numerical modeling of the spatial features of dust structures formed in turbulent gas-dust flows and in flows with shock waves.

The SHiP experiment at CERN

The discovery of the Higgs boson has fully confirmed the Standard Model of particles and fields. Nevertheless, there are still fundamental phenomena, like the existence of dark matter and the baryon asymmetry of the Universe, deserving an explanation that could come from the discovery of new particles. Searches for new physics with accelerators are performed at the LHC, looking for high massive particles coupled to matter with ordinary strength. A new experiment at CERN meant to search for very weakly coupled particles in the few GeV mass domain has been recently proposed. The existence of such particles, foreseen in different theoretical models beyond the Standard Model, is largely unexplored. A beam dump facility using high intensity 400 GeV protons is a copious source of such unknown particles in the GeV mass range. The beam dump is also a copious source of neutrinos and in particular it is an ideal source of tau neutrinos, the less known particle in the Standard Model. The neutrino detector can also search for dark matter through its scattering off the electrons. We report the physics potential of the SHiP experiment.

The SHiP physics program

The discovery of the Higgs boson has fully confirmed the Standard Model of particles and fields. Nevertheless, there are still fundamental phenomena, like the existence of dark matter and the baryon asymmetry of the Universe, which deserve an explanation that could come from the discovery of new particles. The SHiP experiment at CERN meant to search for very weakly coupled particles in the few GeV mass domain has been recently proposed. The existence of such particles, foreseen in different theoretical models beyond the Standard Model, is largely unexplored. A beam dump facility using high intensity 400 GeV protons is a copious source of such unknown particles in the GeV mass range. The beam dump is also a copious source of neutrinos and in particular it is an ideal source of tau neutrinos, the less known particle in the Standard Model. Indeed, tau anti-neutrinos have not been directly observed so far. We report the physics potential of such an experiment including the tau neutrino magnetic moment.

Fast timing for collider detectors

Advancements in fast timing particle detectors have opened up new possibilities to design [Formula: see text] collider detectors that fully reconstruct and separate event vertices and individual particles in the time domain. The applications of these techniques are considered for the physics at CEPC.

Functional central limit theorem for Brownian particles in domains with Robin boundary condition

We rigorously derive non-equilibrium space-time fluctuation for the particle density of a system of reflected diffusions in bounded Lipschitz domains in $\mathbb R^d$. The particles are independent and are killed by a time-dependent potential which is asymptotically proportional to the boundary local time. We generalize the functional analytic framework introduced by Kotelenez [19, 20] to deal with time-dependent perturbations. Our proof relies on Dirichlet form method rather than the machineries derived from Kotelenez's sub-martingale inequality. Our result holds for any symmetric reflected diffusion, for any bounded Lipschitz domain and for any dimension $d\geq 1$.

Accuracy Improvement of GPS Positioning Based on GA-Aided Particle Filter

Aiming at the weight degeneracy phenomena in particle filter algorithm, a resampling method improving the diversity based on GA-aided particle filter was presented. Taking the advantage of genetic algorithm ( GA ) in selection ,crossover and inheritance to make up for the shortcoming of resampling. Genetic operation on particles in real number domain is adapted to reduce the complex of the genetic algorithm. And the evolutionary idea of genetic algorithm was combined with particle filter, by using selection, and mutation to improve the weight degeneracy and diversity of particle filter. This GA-aided particle filter was applied in the established GPS system nonlinear dynamic state space model. The experimental results based on the collected real GPS data is compared with the tradition particle filter, and compared with the effective number of particles and particle distribution. The experimental results indicated that the GA-aided particle filter can increase the number of particle, and effectively solve the particle degradation phenomena, the estimation accuracy of GA-aided particle filter is better than that of particle filter (PF).

Particle refining and coarsening method for smoothed particle hydrodynamics simulations

A particle refining and coarsening method for smoothed particle hydrodynamics is presented and applied to fluid mechanics. This method enables the particles to be refined or coarsened until a proper resolution of particle density for the geometry is achieved, which leads to a higher efficiency despite using a smaller number of particles in the whole simulation domain. The performance of this method is studied through some examples of shear cavities.

Corrigendum to “Heat and Mass Transfer Effect on MHD Flow of a Viscoelastic Fluid through a Porous Medium Bounded by an Oscillating Porous Plate in Slip Flow Regime”

Unsteady flow of an eclectically conducting and incompressible viscoelastic liquid of the Walter model with simultaneous heat and mass transfer near an oscillating porous plate in slip flow regime under the influence of a transverse magnetic field of uniform strength is presented. The governing equations of the flow field are solved by a regular perturbation method for small elastic parameter, and the expressions for the velocity, temperature, concentration, skin friction , the heat flux in terms of the Nusselt number , and the rate of mass transfer in terms of the Sherwood number are obtained. The effects of the important flow parameters on the dynamics are discussed. Findings of the study reveal that the rarefaction parameter accelerates the fluid particles in the flow domain. Elastic parameter contributes to sudden fall of the velocity near the plate. Magnetic force contributes to greater skin friction as the time elapses. Destructive reaction reduces, whereas generative reaction enhances the concentration distribution.

An Improved EEG Feature Extraction Method Based on Quantum Particle Swarm Optimizer Algorithm

Feature extraction is a very important step in P300based brain-computer interface(BCI). Aiming at the large amount ofelectroencephalogram(EEG) data used in BCI, a method which combined quantum particleswarm optimizer(QPSO) algorithm with independent component analysis(ICA) technique was putforward for P300 extraction.It initialized several particles in the feasible domain of de-mixing matrixw, making them parallel search, andfinally reached the global convergence by the information communication betweenthe current particle and the global optimum particle. The method also optimizedthe search strategy using quantum computing to impelICA iterationto converge faster. It was test on real EEG datasetand a simple linear classifier was employed.The recognition accuracy arrivedat 94.5% with 15 times averaged. The results showed thatthe proposed method could extract P300 rapidly and efficiently.It provided a better way to research and develop BCI systemfurther.

Symmetry Breaking in Quasi-1D Coulomb Systems

Quasi one-dimensional systems are systems of particles in domains which are of infinite extent in one direction and of uniformly bounded size in all other directions, e.g. on a cylinder of infinite length. The main result proven here is that for such particle systems with Coulomb interactions and neutralizing background, the so-called "jellium", at any temperature and at any finite-strip width there is translation symmetry breaking. This extends the previous result on Laughlin states in thin, two-dimensional strips by Jansen, Lieb and Seiler (2009). The structural argument which is used here bypasses the question of whether the translation symmetry breaking is manifest already at the level of the one particle density function. It is akin to that employed by Aizenman and Martin (1980) for a similar statement concerning symmetry breaking at all temperatures in strictly one-dimensional Coulomb systems. The extension is enabled through bounds which establish tightness of finite-volume charge fluctuations.

Dynamics of Magnetic Particles in a Magnetic Separation System Using the Finite Element Field Model and Level Set Method

Magnetic particles have been being adopted in various areas, ranging from engineering to biomedical fields such as magnetorheological fluids, separation of magnetically tagged DNA, drug delivery and identification of biological species. The particles in a fluid domain can be controlled by an external magnetic field. The magnetized particles also interact between themselves, revealing interesting characteristics. Many theoretical and computational studies have been presented to analyze these characteristics. However, since most of them are based on the point-dipole model, the mutual interaction between the particles has not been taken into account with a high degree of accuracy. In this paper, we propose a new analysis method for particle interaction based on full magnetic field calculation. The particle system is modeled using the finite element method for magnetic field analysis. This is coupled with the level set method that can effectively capture moving geometry of ferromagnetic particles. Numerical results of two test problems of particle chaining and magnetic separation validate this approach for the system dynamics of magnetic particles.

Phosphatidylserine-positive particles in the apical domain of sensory hair cells

Apical membrane recycling has been proposed to be important for normal hair cell function. The current study reports an in vitro work that demonstrates the presence of phosphatidylserine (PS) and PS-positive vesicles labeled by Annexin V in the apical portion of hair cells. The following characteristics of the PS-positive vesicles were noticed using scanning confocal fluorescence microscopy:(1) variable sizes around 200 nm; (2) variable distribution patterns (either uniformly along individual stereocilia in the hair bundle or irregular) in the stereocilia from cell to cell; (3) variable sizes and numbers at locations along the border of the cuticular plate (CP), with a large number of them located at the vestigal kinocilial location; (4) motility with some of the vesicles during the observation period; (5) increase in PS labeling and the number of PS-positive vesicles after loud sound stimulation; and (6) decreased PS labeling and PS-positive vesicle numbers following treatment with LY-294002, a PI3 -kinase inhibitor. These results suggest that the presence of PS-positive vesicles at the apical area of hair cells may be indicative of vesicle shedding or transportation of a protein or rafts.