Particle Systems Research Articles

A universal mechanism for the development of self-ordering processes in systems of identical particles

Based on the hypothesis of the existence of wave long-range action and its role in the development of self-ordering processes, expressed by us in previous works, we carried out a methodological analysis of the applicability of wave representations in systems of identical particles. The main attention in the analysis is paid to the practical application of de Broglie waves in systems of interacting particles. For this purpose, Bohr's theory of the hydrogen atom has been revised and inconsistencies that contradict modern ideas have been corrected. Two conclusions are made: wave representations of particles are of a material nature; the de Broglie wavelength should be determined in terms of the relative momentum of the interacting particles . Based on the materiality of wave representations, the features of the long-wave interaction of particles are determined. It is emphasized that in many manifestations this interaction has a resonant character. This interaction is the foundation for the universal mechanism for the development of self-ordering processes in systems of identical particles. The paper presents an algorithm for the emergence and functioning of a universal mechanism. The condition for the emergence is always any withdrawal of the system from the state of isotropic chaos and the formation of an initial subgroup of particles with the same magnitude and direction of momenta. The most common cause leading to the fulfillment of the condition is monoenergization of the spectrum of particles in the system. The causative agent of the mechanism is direct collisions of particles, in which the probability of long-wavelength representations is the highest. The development of the mechanism is limited only by the fulfillment of the percolation condition - overcoming the percolation threshold. The final stage of the mechanism is the formation of a superfluid component - a kind of macroparticle that does not interact with walls and other molecules. A characteristic wave feature of such a macroparticle is its coherence. Several final stages in the development of self-ordering processes for typical special phenomena in systems of identical particles are analyzed.

(Part. Part. Syst. Charact. 6/2023)

Particle & Particle Systems CharacterizationVolume 40, Issue 6 2370011 Cover PictureFree Access (Part. Part. Syst. Charact. 6/2023) First published: 18 June 2023 https://doi.org/10.1002/ppsc.202370011AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Cover image provided courtesy of Tymish Y. Ohulchanskyy, Junle Qu, Anderson S. L. Gome, and co-workers. Volume40, Issue6June 20232370011 RelatedInformation

Masthead: (Part. Part. Syst. Charact. 6/2023)

Particle & Particle Systems CharacterizationVolume 40, Issue 6 2370012 MastheadFree Access Masthead: (Part. Part. Syst. Charact. 6/2023) First published: 18 June 2023 https://doi.org/10.1002/ppsc.202370012AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume40, Issue6June 20232370012 RelatedInformation

Many body density of states of a system of non interacting spinless fermions

The modeling of out-of-equilibrium many-body quantum systems requires to go beyond low-energy physics and single or few bodies densities of states. Many-body localization, presence or lack of thermalization and quantum chaos are examples of phenomena in which states at different energy scales, including highly excited ones, contribute to dynamics and therefore affect the system’s properties. Quantifying these contributions requires the many-body density of states (MBDoS), a function whose calculation becomes challenging even for non-interacting identical particles due to the difficulty to enumerate accessible states while enforcing the exchange symmetry. In the present work, we introduce a new approach to evaluate the MBDoS in the general case of non-interacting systems of identical quantum particles. The starting point of our method is the principal component analysis of a filling matrix F describing how N particles can be distributed into L single-particle energy levels. We show that the many body spectrum can be expanded as a weighted sum of singular vectors of the filling matrix. The weighting coefficients only involve renormalized energies obtained from the single body spectrum. We illustrate our method in two classes of problems that are mapped into spinless fermions : (i) non-interacting electrons in a homogeneous tight-binding model in 1D and 2D, and (ii) interacting spins in a chain under a transverse field.

Learning theory for inferring interaction kernels in second-order interacting agent systems

Modeling the complex interactions of systems of particles or agents is a fundamental problem across the sciences, from physics and biology, to economics and social sciences. In this work, we consider second-order, heterogeneous, multivariable models of interacting agents or particles, within simple environments. We describe a nonparametric inference framework to efficiently estimate the latent interaction kernels which drive these dynamical systems. We develop a learning theory which establishes strong consistency and optimal nonparametric min–max rates of convergence for the estimators, as well as provably accurate predicted trajectories. The optimal rates only depends on intrinsic dimension of interactions, which is typically much smaller than the ambient dimension. Our arguments are based on a coercivity condition which ensures that the interaction kernels can be estimated in stable fashion. The numerical algorithm presented to build the estimators is parallelizable, performs well on high-dimensional problems, and its performance is tested on a variety of complex dynamical systems.

Scaling limit of moderately interacting particle systems with singular interaction and environmental noise

We consider moderately interacting particle systems with singular interaction kernel and environmental noise. It is shown that the mollified empirical measures converge in strong norms to the unique (local) solutions of nonlinear Fokker-Planck equations. The approach works for the Biot-Savart and repulsive Poisson kernels.

On the well-posed variational principle in degenerate point particle systems using embeddings of the symplectic manifold

Abstract A methodology on making the variational principle well-posed in degenerate systems is constructed. In the systems including higher-order time derivative terms being compatible with Newtonian dynamics, we show that a set of position variables of a coordinate system of a given system has to be fixed on the boundaries and that such systems are always Ostrogradski stable. For these systems, Frobenius integrability conditions are derived in explicit form. Relationships between integral constants indicated from the conditions and boundary conditions in a given coordinate system are also investigated by introducing three fundamental correspondences between Lagrange and Hamilton formulation. Based on these ingredients, we formulate problems that have to be resolved to realize the well-posedness in the degenerate systems. To resolve the problems, we compose a set of embeddings that extract a subspace holding the symplectic structure of the entire phase space in which the variational principle should be well-posed. Using these embeddings, we establish a methodology to set appropriate boundary conditions that the well-posed variational principle demands. Finally, we apply the methodology to examples and summarize this work as a three-step procedure such that one can use just by following it.

Computer Simulation and Visualization of Wheel Tracks on Solid Surfaces in Virtual Environment

The paper describes methods and approaches for implementation of traces from virtual prototypes of wheeled technical vehicles on solid objects in three-dimensional virtual scenes. Use of particle systems hierarchically linked to wheel models is the basis of solutions developed. Original type of particle system is proposed, in which generation of elements is regulated by special control signal. Creation, processing, storage and destruction of particles is performed by means of the CUDA architecture with use of computing cores of modern graphics processor and video memory resources. Creating geometric model of each particle and its rendering is carried out on programmable graphics pipeline with own vertex, geometry and fragment shaders. Visualization of virtual environment with simulation of wheel tracks is performed in real time. Software modules were created based on developed methods and approaches. Approbation of them was carried out in virtual environment system VirSim and showed adequacy and effectiveness of proposed solutions for simulation of wheel tracks in virtual environment systems and training complexes.

Dynamics of semiflexible ring polymer in chiral Brownian particles bath

Molecular dynamics simulations are used to investigate the dynamic behavior of ring polymer in a bath containing a high concentration of chiral Brownian particles. The chiral Brownian particles around the chain drive the ring polymer to deform, and torque acting on ring is produced and drive the ring to rotate in one direction. Furthermore, the relationship between the ring's rotation speed and the angular velocity of the surrounding Brownian particles is not monotonically linear. The rotation of the ring chain increases initially, then decreases as the angular velocity of the Brownian particle increases. These findings offer a theoretical framework for investigating the conformation and motion properties of polymer macromolecules in Brownian particle systems.

Particle size distribution measurement based on the angular scattering efficiency factor spectra inversion-simulation and experiment.

The quantification of the particle size distribution (PSD) within a particle system is significant to various domains, including atmospheric and environmental sciences, material science, civil engineering, and human health. The scattering spectrum reflects the PSD information of the particle system. Researchers have developed high-precision and high-resolution PSD measurements for monodisperse particle systems through scattering spectroscopy. However, for polydisperse particle systems, current methods based on light scattering spectrum and Fourier transform analysis can only obtain the information of the particle component, but cannot provide the relative content information of each component. In this paper, a PSD inversion method based on the angular scattering efficiency factors (ASEF) spectrum is proposed. By establishing a light energy coefficient distribution matrix, and then measuring the scattering spectrum of the particle system, PSD can be measured in conjunction with inversion algorithms. The simulations and experiments conducted in this paper substantiate the validity of the proposed method. Unlike the forward diffraction approach that measures the spatial distribution of scattered light I(θ) for inversion, our method uses the multi-wavelength distribution information of scattered light β(λ). Moreover, the influences of noise, scattering angle, wavelength, particle size range, and size discretization interval on PSD inversion are studied. The method of condition number analysis is proposed to identify the appropriate scattering angle, particle size measurement range, and size discretization interval, and it can reduce the root mean square error(RMSE) of PSD inversion. Furthermore, the method of wavelength sensitivity analysis is proposed to select the spectral band with higher sensitivity to particle size changes, thereby improving the computational speed and avoiding the problem of diminished accuracy caused by the reduction of the number of wavelengths used.

Retrieving mean volumetric properties of multiphase flows from 2D images: A new approach combining deep learning algorithms and 3D modelling

Measuring the morphological properties of complex multiphase systems is a crucial problem in many areas of science and industry and is particularly difficult in dense environments with limited optical access. This paper presents a new approach capable of extracting three-dimensional (3D) information from spherical particle systems based solely on two-dimensional (2D) projections of the system. Synthetic images of the system are generated using a stochastic geometrical model from a simulated 3D particle system with the same geometrical features as the studied system, which is projected into 2D images labeled with the appropriate 3D information. These images are then fed to a convolutional neural network (CNN) for training before being tested on synthetic and experimental images. Validation results show that this technique successfully predicts the mean features of the studied systems, even for dense environments with overlapping particles, with high computational efficiency.

Dynamic Inertia Weight Particle Swarm Optimization for Anomaly Detection: A Case of Precision Irrigation

Anomaly-based Intrusion Detection System (IDS) is a type of IDS that detects abnormal behaviors by analyzing system activity and network traffic. Anomaly-based IDS works by establishing a baseline of normal behavior for a system or a network. However, these types of systems are less used compared to signature-based IDS for one primary challenge: How to define this normal behavior baseline? The answer to this question is complicated, since it involves not only analyzing or learning from historical data, but requires and understanding of the business domain the system is implemented in. The present study proposes a novel approach to constructing an unsupervised data classifier that combines both Particle Swarm Optimization (PSO) and clustering techniques for anomaly detection. The primary objective of this methodology is to surmount the limitations that conventional clustering algorithms suffer from, such as their inability to identify non-linear patterns within the data, susceptibility to initial conditions, and difficulty in overcoming the problem of local optima. The concept of particle systems is discussed by examining their origins, search strategies, and convergence mechanisms. We use a variant of the Particle Swarm Optimization called Dynamic Inertia Weight-Particle Swarm optimization (DIW-PSO) for our clustering process, and we elaborate on the reasoning behind this decision. Subsequently, we describe the labeling algorithm used for the resulting clusters and we explain the process for identifying anomalous clusters. We have demonstrated the effectiveness of our method by applying it to an intelligent irrigation control system for cotton plants. The results show that our classifier was able to accurately detect abnormal patterns that deviated from the optimal water requirements and growth conditions of the plants.

Occurrence of connected clusters in motility-induced phase-separated states of persistent active particles at zero temperature (a)

To study the interplay of the formation of clusters of overlapping particles and motility-induced phase separation in the zero temperature limit in two dimensions, we consider a simple model system consisting of a bidisperse mixture of disks that are only subject to a repulsion force in case of overlaps and an active force. The orientation of the disks is chosen randomly in the beginning and does not change anytime during the simulation thus corresponding to an infinite persistence length. Simulations with our model system reveal that clusters of overlapping particles occur in the dense phase of a phase-separated state in case of intermediate values of the ratio of active to repulsive force. However, for smaller activities there are only a few overlaps between the particles in the dense phase and the coexistence region ends at a packing fraction below the onset of the formation of clusters with overlapping particles. For large activities small clusters corresponding to small patches of the dense phase are found that are unstable due to the activity. Finally, the occurrence of connected clusters affects the phase separation. Our findings on how cluster formation and phase separation are related are relevant to many active particle systems as after adding thermal noise our model system corresponds to a system of active Brownian particles that have been intensely studied in recent years. Further extensions by adding friction, inertia, or more complex interactions are possible.

DIFFICULTIES IN TRANSLATING RUSSIAN PARTICLES INTO PERSIAN

Оne of the most dif cult problems when translating literary texts from Russian into Persian is to understand the meaning of Russian particles and f nd suitable equivalents. Particles are important elements of the text that concretize, separate and strengthen any words according to the author’s choice. T e abundance of particles in Russian and the important role of particles in the expression of emotional-expressive shades of sentences on the one hand, and their small number and limited use in the Persian language on the other, creates serious problems in understanding their meaning, especially in translation. T e concept of particles relates to dif erent terms in the Persian language that do not adequately cover Russian particles, which determines the relevance of the work. In most cases, the Persians refer to Russian particles as untranslatable parts of speech that do not have a special, signif cant function. In this paper, the particle systems of the Russian and Persian languages are described by means of a comparative method and then a number of translations of some examples from Russian f ction into Persian are analyzed in order to identify dif culties faced by Iranian students and translators in understanding the meaning and translation of Russian particles. T e authors show that the main dif culties faced by Iranian translators are the incorrect dif erentiation of particles from image/svg+xml homonymic constructions, incorrect def nition of a clear meaning of particles in a sentence, the inability to f nd a suitable translation equivalent or the inability to f nd a suitable equivalent in accordance with the semantics and style in the Persian language and reinterpretation of the meaning of particles.

Generalized gradient structures for measure-valued population dynamics and their large-population limit

We consider the forward Kolmogorov equation corresponding to measure-valued processes stemming from a class of interacting particle systems in population dynamics, including variations of the Bolker–Pacala–Dieckmann-Law model. Under the assumption of detailed balance, we provide a rigorous generalized gradient structure, incorporating the fluxes arising from the birth and death of the particles. Moreover, in the large population limit, we show convergence of the forward Kolmogorov equation to a Liouville equation, which is a transport equation associated with the mean-field limit of the underlying process. In addition, we show convergence of the corresponding gradient structures in the sense of Energy-Dissipation Principles, from which we establish a propagation of chaos result for the particle system and derive a generalized gradient-flow formulation for the mean-field limit.

Discrete element simulation of the breakage behavior of porous granules utilizing bond models

AbstractIn the development of ships, the aspect of collision safety plays a major role. In this paper, the approach to improve the crashworthiness of ships is to fill the already existing double hull with a granular material. For this purpose, the material used is to be investigated numerically. A discrete element approach is applied for this in the paper presented. Since the breakage behavior of the particles is of particular interest, the discrete element method is used together with a bonded particle method. This approach is extended by a model which is able to take into account the micro-cracks occurring in the material and the energy dissipation caused by them. Using this and other models of the bonded particle method, simulations are carried out and compared with corresponding experiments. A particular focus is on uniaxial compression tests with single particles and particle systems. The single-particle tests are used to determine various simulation parameters relevant to individual particles in terms of geometry and material. In addition, the multi-particle tests provide insight into the behavior of several particles, their interaction and dynamics. These simulations can be used to test the extent to which it is possible to represent porous particles and their fracture behavior by numerical means.

Moments of Random Quantum Marginals via Weingarten Calculus

Abstract The randomized quantum marginal problem asks about the joint distribution of the partial traces (“marginals”) of a uniform random Hermitian operator with fixed spectrum acting on a space of tensors. We introduce a new approach to this problem based on studying the mixed moments of the entries of the marginals. For randomized quantum marginal problems that describe systems of distinguishable particles, bosons, or fermions, we prove formulae for these mixed moments, which determine the joint distribution of the marginals completely. Our main tool is Weingarten calculus, which provides a method for computing integrals of polynomial functions with respect to Haar measure on the unitary group. As an application, in the case of two distinguishable particles, we prove some results on the asymptotic behavior of the marginals as the dimension of one or both Hilbert spaces goes to infinity.

Functional Ligand-Enabled Particle Assembly for Bio-Nano Interactions.

ConspectusFunctional ligands consist of a wide range of small or large molecules that exhibit a spectrum of physical, chemical, and biological properties. A suite of small molecules (e.g., peptides) or macromolecular ligands (e.g., antibodies and polymers) have been conjugated to particle surfaces for specific applications. However, postfunctionalization of ligands often presents challenges in controlling the surface density and may require the chemical modification of ligands. As an alternative option to postfunctionalization, our work has focused on using functional ligands as building blocks to assemble particles while maintaining their intrinsic (functional) properties. Through self-assembly or template-mediated assembly strategies, we have developed a range of protein-, peptide-, DNA-, polyphenol-, glycogen-, and polymer-based particles. This Account discusses the assembly of such nanoengineered particles, which includes self-assembled nanoparticles, hollow capsules, replica particles, and core-shell particles, according to three categories of functional ligands (i.e., small molecules, polymers, and biomacromolecules) that are used as building blocks for their formation. We discuss a range of covalent and noncovalent interactions among ligand molecules that have been explored to facilitate the assembly of particles. The physicochemical properties of the particles, including size, shape, surface charge, permeability, stability, thickness, stiffness, and stimuli-responsiveness, can be readily controlled by varying the ligand building block or by tuning the assembly method. By selecting specific ligands as building blocks, the bio-nano interactions (i.e., stealth, targeting, and cell trafficking) can also be modulated. For instance, particles composed mainly of low-fouling polymers (i.e., poly(ethylene glycol)) exhibit an extended blood circulation time (half-life > 12 h), while antibody-based nanoparticles demonstrate that a trade-off between stealth and targeting may be required when designing targeting nanoparticle systems. Small molecular ligands, such as polyphenols, have been used as building blocks for particle assembly as they can interact with various biomacromolecules through multiple noncovalent interactions, retain the function of biomacromolecules within the assembly, enable pH-responsive disassembly when coordinating with metal ions, and facilitate endosomal escape of nanoparticles. A perspective is provided on the current challenges associated with the clinical translation of ligand-based nanoparticles. This Account is also expected to serve as a reference to guide the fundamental research and development of functional particle systems assembled from various ligands for diverse applications.

Nonparametric adaptive estimation for interacting particle systems

AbstractWe consider a stochastic system of interacting particles with constant diffusion coefficient and drift linear in space, time‐depending on two unknown deterministic functions. Our concern here is the nonparametric estimation of these functions from a continuous observation of the process on for fixed and large . We define two collections of projection estimators belonging to finite‐dimensional subspaces of . We study the ‐risks of these estimators, where the risk is defined either by the expectation of an empirical norm or by the expectation of a deterministic norm. Afterwards, we propose a data‐driven choice of the dimensions and study the risk of the adaptive estimators. The results are illustrated by numerical experiments on simulated data.

Reaction diffusion systems and extensions of quantum stochastic processes

Reaction diffusion systems describe the behaviour of dynamic, interacting, particulate systems. Quantum stochastic processes generalise Brownian motion and Poisson processes, having operator valued Itô calculus machinery. Here it is shown that the three standard noises of quantum stochastic processes can be extended to model reaction diffusion systems, the methods being exemplified with spatial birth–death processes. The usual approach for these systems are master equations, or Doi-Peliti path integration techniques. The machinery described here provide efficient analyses for many systems of interest, and offer an alternative set of tools to investigate such problems.