Particle Systems Research Articles

Single file motion of robot swarms

We present experimental results on the single file motion of a group of robots interacting with each other through position sensors. We successfully replicate the fundamental diagram typical of these systems, with a transition from free flow to congested traffic as the density of the system increases. In the latter scenario we also observe the characteristic stop-and-go waves. The unique advantages of this novel system, such as experimental stability and repeatability, allow for extended experimental runs, facilitating a comprehensive statistical analysis of the global dynamics. Above a certain density, we observe a divergence of the average jam duration and the average number of robots involved in it. This discovery enables us to precisely identify another transition: from congested intermittent flow (for intermediate densities) to a totally congested scenario for high densities. Beyond this finding, the present work demonstrates the suitability of robot swarms to model complex behaviors in many particle systems. Published by the American Physical Society 2024

Reduced Variance Random Batch Methods for Nonlocal PDEs

Random Batch Methods (RBM) for mean-field interacting particle systems enable the reduction of the quadratic computational cost associated with particle interactions to a near-linear cost. The essence of these algorithms lies in the random partitioning of the particle ensemble into smaller batches at each time step. The interaction of each particle within these batches is then evolved until the subsequent time step. This approach effectively decreases the computational cost by an order of magnitude while increasing the amount of fluctuations due to the random partitioning. In this work, we propose a variance reduction technique for RBM applied to nonlocal PDEs of Fokker-Planck type based on a control variate strategy. The core idea is to construct a surrogate model that can be computed on the full set of particles at a linear cost while maintaining enough correlations with the original particle dynamics. Examples from models of collective behavior in opinion spreading and swarming dynamics demonstrate the great potential of the present approach.

Evaluation and correction of laser scattering-based particle size measurements at high obscurations using the Monte Carlo method

High turbidity and its associated multiple scattering phenomena can often lead to an underestimation of the particle size for the laser scattering method. To investigate the light scattering characteristics and evaluate the effect of high-obscuration particle systems, a Monte Carlo model has been developed based on Mie’s theory. A compact setup was utilized to perform a series of experiments on three certified reference materials (CRMs) at different concentrations. Both the scattered light energy distribution and the obscuration were measured simultaneously. The inversion results of the particle size indicate a continuous increase in deviation from the nominal value as the obscuration rises. According to the conventional single scattering model, the inversion errors fall within 5% for obscuration levels ranging from 0.15 to 0.2. However, for a higher obscuration, the error can reach approximately 15%. Thus, a correction method has been proposed by introducing an improved model matrix that includes the multiple scattering contribution for the data inversion, which exhibits a significant enhancement in the accuracy of particle size measurements under high obscuration conditions. For all three types of particles being studied, the error was successfully reduced to within 5.0%.

An electro-mechanical contact model for particulate systems

This study presents an electro-mechanical contact model to investigate the electrical response within a particulate system under mechanical loading. The model considers the electrical resistance across particle-to-particle and particle-to-wall contacts by formulating bulk and contact resistances, separately. Hertz and Holm's models are utilised for the contact resistance in the overlapping region. The bulk resistance for the particles is estimated considering particle geometry transformations during the compression. The proposed electro-mechanical contact model is verified and validated against published analytical solutions and experimental data. Finally, the contact model is applied to simulate a high-pressure torsion test to investigate the influence of three different materials on the electrical conduction properties of the system after particle fragmentation. The intensity distributions of electric potential, current and resistance of the granular system are analysed at both particle and system scales. An electroactivity index is proposed for evaluating the contribution of each fragment sieve cut on the bulk electrical conduction behaviour.

Switching particle systems for foraging ants showing phase transitions in path selections

Switching interacting particle systems studied in probability theory are the stochastic processes of hopping particles on a lattice made up of slow and fast particles, where the switching between these types of particles occurs randomly at a given transition rate. This paper explores how such stochastic processes involving multiple particles can model group behaviors of ants. Recent experimental research by the last author’s group has investigated how ants switch between two types of primarily relied cues to select foraging paths based on the current situation. Here, we propose a discrete-time interacting random walk model on a square lattice, incorporating two types of hopping rules. Numerical simulation results demonstrate global changes in selected homing paths, transitioning from trailing paths of the ‘pheromone road’ to nearly optimal paths depending on the switching parameters. By introducing two types of order parameters characterizing the dependence of homing duration distributions on switching parameters, we discuss these global changes as phase transitions in ant path selections. We also study critical phenomena associated with continuous phase transitions.

Lane formation in an active particle model with chirality for pedestrian traffic

We analyze the pattern formation in systems of active particles with chiral forces in the context of pedestrian dynamics. To describe the interparticle interactions, we use the standard social force model and supplement it with a new type of force that reflects chirality. We perform numerical simulations of two pedestrian flows moving in opposite directions along a corridor. We observe two dynamic phase transitions that occur for varying number densities of particles and strengths of the chirality force: one from disordered motion to multi-lane motion and another from multi-lane to two-lane motion. We develop a qualitative theory that describes the demarcation lines for these phase transitions in the phase diagram chirality-density. The results of our analysis agree fairly well with the simulation data. A comparison with previously reported experimental data has been provided. Our findings may find applications in urban and transportation-planning problems.

Towards complex dynamic physics system simulation with graph neural ordinary equations

The great learning ability of deep learning facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour both in academia and industry. However, the complex laws of the physical world pose significant challenges to the learning based simulations, such as the varying spatial dependencies between interacting particles and varying temporal dependencies between particle system states in different time stamps, which dominate particles’ interacting behavior and the physical systems’ evolution patterns. Existing learning based methods fail to fully account for the complexities, making them unable to yield satisfactory simulations. To better comprehend the complex physical laws, we propose a novel model – Graph Networks with Spatial–Temporal neural Ordinary Differential Equations (GNSTODE) – that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework. Through training with real-world particle–particle interaction observations, GNSTODE can simulate any possible particle systems with high precisions. We empirically evaluate GNSTODE’s simulation performance on two real-world particle systems, Gravity and Coulomb, with varying levels of spatial and temporal dependencies. The results show that GNSTODE yields better simulations than state-of-the-art methods, showing that GNSTODE can serve as an effective tool for particle simulation in real-world applications. Our code is made available at https://github.com/Guangsi-Shi/AI-for-physics-GNSTODE.

Enhanced Tracer Particle Detection in Dynamic Bulk Systems Based on Polarimetric Radar Signature Correlation.

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes.

Hydrodynamic fluctuations, diffusion and correlation functions in systems of particles adsorbed on solid surfaces

We present the detailed derivation of the equations describing the evolution of the hydrodynamic fluctuations of the coverage of particles adsorbed on homogeneous lattices. Using the method of the non-equilibrium statistical operator, we reduce the balance equation governing the behavior of the individual particles to the diffusion equation. On a macroscopic level, this equation describes the approach to equilibrium of the hydrodynamic coverage fluctuations. We obtain the analytical expressions for the Fickian diffusivity and Onsager phenomenological coefficient. These expressions are derived with account of the lateral interaction between the particles. They are simple functions of the thermodynamic quantities — derivatives of the thermodynamic potential over its arguments. The transport coefficients accurately describe the development of fluctuations in the entire coverage region and in the wide range of lateral interactions. We presented an elementary introduction to the theory of fluctuations in the lattice gas systems. For calculations of the correlation function and spectral density of fluctuations, we use the Langevin approach and the method of moments. The exact coincidence of the analytical expressions for the diffusion coefficients obtained by the two independent calculations is the direct proof of the accuracy of the approach developed in Chumak and Tarasenko (1980).

Neural-network-based drag force model for polydisperse assemblies of irregular-shaped particles

In this study, a drag force model of irregular-shaped particles in gas-solid flows is investigated using neural network approaches. Pre-developed variational autoencoder, multi-layer perceptron model, and transpose convolutional neural-network model are utilized to obtain intermediate parameters of a pair-wise interaction point-particle (PIEP) model. These parameters are utilized to predict individual drag force coefficients of polydisperse, particulate flows. To apply the PIEP-based model to medium-concentration flows, the average drag force coefficients are obtained through an in-house particle-resolved direct numerical simulation, and a regressed model is developed to predict solid volume fraction factors. With the regression model, the PIEP-based model using the neural-network approaches shows good prediction in particulate systems from low to intermediate concentrations. This study provides computationally efficient models of practical, realistic particulate systems for large scale fluid dynamics simulation, in that it allows predicting the drag forces of polydisperse particles with a small amount of data.

A NEW THEORY OF PARTICLE PHYSICS ABOUT COUPLING SYSTEM AND REGULAR MULTIVARIATE STATE

In this paper, some particle science issues are considered and studied: Why the electric charge can not be fraction for any particles? Why composite particles are easy to split and the mass has decreased? Why the fundamental particles with identical quantum numbers have the unequal mass? These scientific problems are solved by establishing new particle theories: The mechanism of regular multivariate states is proposed to explain the characteristics of fundamental particles, The coupling theory of particle systems is proposed to explain the mass characteristics of composite particles. The scientific analysis of these mechanisms and theories are consistent with the experimental results of many particles, and according to these mechanisms and theories can derive many new particles.

Aquasomes: an updated review

Aquasomes are circular particles that serve as nano particulate carrier systems, consisting of a solid phase nanocrystalline core coated in an oligomeric film. These three-layered self-assembled structures, ranging from 60 to 300 nanometers in size, allow direct or indirect absorption of biochemically active molecules. Aquasomes can be loaded with large amounts of agents through entropic, van der Waals, and ionic forces due to their size and active surface area. They display colloidal physical characteristics in an aqueous medium and are effective in distributing insulin, hemoglobin, serratiopeptidase, and poorly water-soluble medications.eramic nanoparticles, also known as aquasomes, are spherical particles that self-assemble into nanoparticulate carriers, resembling water bodies. These self-assembled structures are effective in the distribution of hemoglobin, insulin, and enzymes like serratiopeptidase. In this abstract, we will cover a review on Aquasomes.

Physics Based Animation Using Computer Graphics

Physics-based animation is a multidisciplinary area that uses ideas from physics, computer science, and mathematics to create realistic and dynamic movements in virtual settings. The basic ideas and methods of physics-based animation are introduced in this research paper, with an emphasis on how mathematical models and physical laws are used to produce realistic motion in computer-generated images. To construct realistic virtual environments, key disciplines covered include fluid simulation, fabric simulation, rigid body dynamics, and soft body dynamics. The report highlights recent developments in real-time physics simulations and addresses other topics such as processing economy, accuracy, and scalability. Applications for physics-based animation can be found in many different fields, such as virtual reality, simulation training, movies, and video games. The pursuit of more precise and effective physics-based animation is essential as technology develops to produce captivating and realistic virtual worlds. Keywords— Physics-based animation(PBA), virtual environment, Physics simulation, Dynamics, Animation software, Real-time physics, Collision detection, Fluid dynamics, Particle systems, Cloth simulation, Smooth particle hydrodynamics, finite element method, Navier-Stokes, Rigid body dynamics, Soft body dynamics, Deformation, Motion capture, Character animation, Kinematics, Rendering, Game development, Virtual reality, Augmented reality, Computer graphics, Simulation accuracy

Wigner- and Marchenko–Pastur-Type Limit Theorems for Jacobi Processes

AbstractWe study Jacobi processes $$(X_{t})_{t\ge 0}$$ ( X t ) t ≥ 0 on $$[-1,1]^N$$ [ - 1 , 1 ] N and $$[1,\infty [^N$$ [ 1 , ∞ [ N which are motivated by the Heckman–Opdam theory and associated integrable particle systems. These processes depend on three positive parameters and degenerate in the freezing limit to solutions of deterministic dynamical systems. In the compact case, these models tend for $$t\rightarrow \infty $$ t → ∞ to the distributions of the $$\beta $$ β -Jacobi ensembles and, in the freezing case, to vectors consisting of ordered zeros of one-dimensional Jacobi polynomials. We derive almost sure analogues of Wigner’s semicircle and Marchenko–Pastur limit laws for $$N\rightarrow \infty $$ N → ∞ for the empirical distributions of the N particles on some local scale. We there allow for arbitrary initial conditions, which enter the limiting distributions via free convolutions. These results generalize corresponding stationary limit results in the compact case for $$\beta $$ β -Jacobi ensembles and, in the deterministic case, for the empirical distributions of the ordered zeros of Jacobi polynomials. The results are also related to free limit theorems for multivariate Bessel processes, $$\beta $$ β -Hermite and $$\beta $$ β -Laguerre ensembles, and the asymptotic empirical distributions of the zeros of Hermite and Laguerre polynomials for $$N\rightarrow \infty $$ N → ∞ .

Designing a set of criteria for evaluating artificial neural networks trained with physics-based data to replicate molecular dynamics and other particle method trajectories

This article presents an in-depth analysis and evaluation of artificial neural networks (ANNs) when applied to replicate trajectories in molecular dynamics (MD) simulations or other particle methods. This study focuses on several architectures—feedforward neural networks (FNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), time convolutions (TCs), self-attention (SA), graph neural networks (GNNs), neural ordinary differential equation (ODENets), and an example of physics-informed machine learning (PIML) model—assessing their effectiveness and limitations in understanding and replicating the underlying physics of particle systems. Through this analysis, this paper introduces a comprehensive set of criteria designed to evaluate the capability of ANNs in this context. These criteria include the minimization of losses, the permutability of particle indices, the ability to predict trajectories recursively, the conservation of particles, the model’s handling of boundary conditions, and its scalability. Each network type is systematically examined to determine its strengths and weaknesses in adhering to these criteria. While, predictably, none of the networks fully meets all criteria, this study extends beyond the simple conclusion that only by integrating physics-based models into ANNs is it possible to fully replicate complex particle trajectories. Instead, it probes and delineates the extent to which various neural networks can “understand” and interpret aspects of the underlying physics, with each criterion targeting a distinct aspect of this understanding.

Relative Humidity-Dependent Phase Transitions in Submicron Respiratory Aerosols.

Respiratory viruses, such as influenza and severe acute respiratory syndrome coronavirus 2, represent a substantial public health burden and are largely transmitted through respiratory droplets and aerosols. Environmental factors such as relative humidity (RH) and temperature impact virus transmission rates, and a precise mechanistic understanding of the connection between these environmental factors and virus transmission would improve efforts to mitigate respiratory disease transmission. Previous studies on supermicrometer particles observed RH-dependent phase transitions and linked particle phase state to virus viability. Phase transitions in atmospheric aerosols are dependent on size in the submicrometer range, and actual respiratory particles are expelled over a large size range, including submicrometer aerosols that can transmit diseases over long distances. Here, we directly investigated the phase transitions of submicrometer model respiratory aerosols. A probe molecule, Nile red, was added to particle systems including multiple mucin/salt mixtures, a growth medium, and simulated lung fluid. For each system, the polarity-dependent fluorescence emission was measured following RH conditioning. Notably, the fluorescence measurements of mucin/NaCl and Dulbecco's modified Eagle's medium particles indicated that liquid-liquid phase separation (LLPS) also occurs in submicron particles, suggesting that LLPS can also impact the viability of viruses in submicron particles and thus affect aerosol virus transmission. Furthermore, the utility of fluorescence-based measurements to study submicrometer respiratory particle physicochemical properties in situ is demonstrated.

GPU-accelerated CFD-DEM modeling of gas-solid flow with complex geometry and an application to raceway dynamics in industry-scale blast furnaces

The coupling of Computational Fluid Dynamics (CFD) and Discrete Element Model (DEM) is a powerful tool for simulating dense particulate systems, yet the conventional CFD-DEM has limits for systems with large particle numbers and complex geometry. This paper reports a novel GPU-based CFD-DEM model to simulate the gas–solid flow with large particle numbers and complex geometry. A novel coupling strategy between the CFD solver and DEM solver is developed, featuring high efficiency and stability. The developed model is validated against the experimental measurements, and its efficiency is compared to the previous CFD-DEM simulations. Then, for demonstration, the model is employed to simulate the dynamic behavior of gas–solid flow in the raceway in ironmaking blast furnaces by considering complex tuyere structure details and the huge particle numbers involved. This model allows to study the effect of the tuyere angle in terms of raceway formulation and tuyere erosion. The results show that the largest and most stable raceway volume can be reached at 5° downward tuyere, although the −5° tuyere nose experiences more wear than 10° downward tuyere. The model provides a cost-effective tool to overcome the longstanding challenge of simulating dense fluid-particle systems with huge particle numbers and complex geometry.

The fractional soliton solutions of dynamical system arising in plasma physics: The comparative analysis

In light of fractional theory, this paper presents several new effective solitonic formulations for the Langmuir and ion sound wave equations. Prior to this study, no previous research has presented the comparision and obtained the generalized fractional soliton solutions of this kind with power law kernel and Mittag-Leffler kernel. The ion sound and Langmuir wave equations are essential in plasma physics, offering insights into the collective behavior of charged particles in plasmas and enabling diagnostics and control of these complex, ionized gas systems. The two distinct fractional order differential operators are substituted for the traditional order derivative to reshape the examined model. The Atangana-Baleanu non-singular and non-local operator and conformable fractional operator are the fractional-order operators that are used to create the fractional complex system equations for Langmuir waves and ion sound. A constructive approach new auxiliary equation method utilizes to obtain the exact analytical soliton solutions for ion sound and Langmuir wave equation. A wide range of soliton solutions is obtained, including mixed complex solitary shock solutions, singular solutions, mixed shock singular solutions, mixed trigonometric solutions, mixed singular solutions, exact solutions, mixed periodic solutions, and mixed hyperbolic solutions, dark soliton, bright soliton, trigonometric solutions, periodic results, and hyperbolic results. The solitons solution of the ion sound and Langmuir wave equations lies in their ability to maintain wave stability, their role in modeling wave propagation and nonlinear effects, their potential use as diagnostic tools, and their relevance in wave-particle interactions in plasma physics. The solitons provide a valuable framework for understanding the behavior of waves in plasmas and offer insights into the complex dynamics of these charged particle systems. A graphical comparison analysis of a few solutions is also shown here, taking into account appropriate parametric values through the use of the software package. Moreover, the results of this study have important implications for Hamilton's equations and generalized momentum, where solitons are employed in long-range interactions.

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit

Chemical simulation teaching system based on virtual reality and gesture interaction

BackgroundMost existing chemical experiment teaching systems lack solid immersive experiences, making it difficult to engage students. To address these challenges, we propose a chemical simulation teaching system based on virtual reality and gesture interaction. MethodsThe parameters of the models were obtained through actual investigation, whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine. By establishing an interface for the physics engine, gesture interaction hardware, and virtual reality (VR) helmet, a highly realistic chemical experiment environment was created. Using code script logic, particle systems, as well as other systems, chemical phenomena were simulated. Furthermore, we created an online teaching platform using streaming media and databases to address the problems of distance teaching. ResultsThe proposed system was evaluated against two mainstream products in the market. In the experiments, the proposed system outperformed the other products in terms of fidelity and practicality. ConclusionsThe proposed system which offers realistic simulations and practicability, can help improve the high school chemistry experimental education.