Particle-based Model Research Articles

Skyrmion soliton motion on periodic substrates by atomistic and particle-based simulations

We compare the dynamical behavior of magnetic skyrmions interacting with square and triangular defect arrays just above commensuration using both atomistic and particle-based models. Under applied drives, the initial motion is a kink traveling through the pinned skyrmion lattice. For the square defect array, both models agree well and show a regime in which the soliton motion is locked along 45°. The atomistic model also produces locking of a soliton along 30°, which is absent in the particle-based model. For the triangular defect array, the atomistic model exhibits 30° soliton motion over a wide region of external current values. In contrast, the particle-based model gives 45° soliton motion over a small range of external driving force values. The difference arises because the nondeforming particle model facilitates meandering skyrmion orbits while the deformable atomistic model enables stronger skyrmion-skyrmion interactions that reduce the meandering. Our results indicate that soliton motion through pinned skyrmion lattices on a periodic substrate is a robust effect.

Weakly nonlinear analysis of minimal models for Turing patterns

The simplest particle-based mass-action models for Turing instability – i.e. those with only two component species undergoing instantaneous interactions of at most two particles, with the smallest number of distinct interactions – fall into a surprisingly small number of classes of reaction schemes. In previous work we have computed this classification, with different schemes distinguished by the structure of the interactions. Within a given class the reaction stoichiometry and rates remain as parameters that determine the linear and nonlinear evolution of the system.Adopting the usual weakly nonlinear scalings and analysis reveals that, under suitable choices of reaction stoichiometry, and in nine of the 11 classes of minimal scheme exhibiting a spatially in-phase (“true activator-inhibitor”) Turing instability, stable patterns are indeed generated in open regions of parameter space via a generically supercritical bifurcation from the spatially uniform state. In three of these classes the instability is always supercritical while in six there is an open region in which it is subcritical. Intriguingly, however, in the remaining two classes of minimal scheme we require different weakly nonlinear scalings, since the coefficient in the usual cubic normal form unexpectedly vanishes identically. In these cases, a different set of asymptotic scalings is required.We present a complete analysis through deriving the normal form for these two cases also, which involves quintic terms. This fifth-order normal form also captures the behaviour along the boundaries between the supercritical and subcritical cases of the cubic normal form. The details of these calculations reveal the distinct roles played by reaction rate parameters as compared to stoichiometric parameters.We quantitatively validate our analysis via numerical simulations and confirm the two different scalings for the amplitude of predicted stable patterned states.

Simulated particle evolution within a winter storm: contributions of riming to radar moments and precipitation fallout

Abstract. Remote sensing radars from airborne and spaceborne platforms provide critical observations of clouds to estimate precipitation rates across the globe. The ability of these radars to detect changes in precipitation properties is advanced by Doppler measurements of particle fall speed. Within mixed-phase clouds, precipitation mass and its fall characteristics are especially sensitive to the effects of riming. In this study, we quantified these effects and investigated the distinction of riming from aggregation in Doppler radar vertical profiles using quasi-idealized particle-based model simulations. Observational constraints of a control simulation were determined from airborne in situ and remote sensing measurements collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) for a wintry–mixed precipitation event over the northeastern United States on 4 February 2022. From the upper boundary of a one-dimensional column, particle evolution was simulated through vapor deposition, aggregation, and riming processes, producing realistic Doppler radar profiles. Despite a modest observed amount of supercooled liquid water (0.05 g m−3), riming accounted for 55 % of the ice-phase precipitation mass, cumulatively increasing reflectivity by 44 % and Doppler velocity by 68 %. Independent evaluation of process-based sensitivities showed that, while radar reflectivity is comparably sensitive to either riming- or aggregation-based particle morphology, the Doppler velocity profile is uniquely sensitive to particle density changes during riming. Thus, Doppler velocity profiles advance the diagnosis of riming as a dominant microphysical process in stratiform clouds from single-wavelength radars, which has implications for quantitative constraints of particle properties in remote sensing applications.

Unraveling the dance of phosphoproteins at adhesion planes: Modeling YAP phosphorylation by a particle-based stochastic model

Unraveling the dance of phosphoproteins at adhesion planes: Modeling YAP phosphorylation by a particle-based stochastic model

A Quantitative Particle-Based Approach for the Geometallurgical Assessment of Tailings Deposits

Tailings generated during ore processing may host significant residual contents of valuable commodities, including critical metals. The particle properties of the tailings, such as mineralogy, particle size, and the surface liberation of ore minerals, strongly control processing behaviour. This study explores a novel combination of methods for incorporating particle data, derived from automated mineralogy, into geometallurgical models of tailings deposits to better understand their reprocessing potential and the economic feasibility of re-mining. This was achieved through binning of different particle types, geostatistical modelling of particle bin frequencies, and bootstrap resampling to reconstruct particle populations. The spatial distributions of processing-relevant particle properties throughout the tailings deposit were predicted with corresponding uncertainties. There are clear systematic trends in the spatial distributions of different particle types, resulting from the sedimentary-style deposition of the tailings. For instance, the tailings nearer the dam walls comprise coarser, silicate-rich particles, while fine-grained and well-liberated sulphide mineral particles are more abundant in the centre of the tailings deposit. As a result, robust models could be developed for the spatial distributions of particle size and mineralogy, which strongly control the sorting of particles during deposition, and other related properties, such as sulphide mineral grain sizes. Finally, a bulk sulphide flotation process was simulated and acid mine drainage potential estimated using the interpolated particle data. Around 58% of the sulphide minerals present could be recoverable by flotation, with the recoverable sulphide portion decreasing towards the centre of the TSF due to the fine-grained nature of the sulphide minerals. The acid mine drainage potential of the tailings is estimated to be moderate to high, indicating that the carbonate minerals present are not sufficient to neutralise the high acid-generating potential of the sulphide minerals. Overall, this study demonstrates how particle-based geometallurgical models can be developed and utilised for practical applications, with the aim of improving the accuracy of resource and reserve estimations of tailings deposits and the sustainable and responsible management of anthropogenic resources. The methodology proposed here can be easily transferred to other tailings deposits.

Kinetic investigation of discharge performance for Xe, Kr, and Ar in a miniature ion thruster using a fast converging PIC-MCC-DSMC model

Abstract The present work develops a full particle-based model that couples the particle-in-cell plus Monte Carlo collision (PIC-MCC) simulation for plasma dynamics and the direct simulation Monte Carlo (DSMC) method for neutral dynamics in a synergistic iterative manner. This new model overcomes the slow convergence issue in the conventional direct coupling approach caused by the disparity of the time scales between the plasma and neutral dynamics. This model is applied to simulate the behavior of xenon (Xe) and its potential alternatives, krypton (Kr) and argon (Ar), in the discharge chamber of a miniature direct current (DC) ion thruster. The results show that a stable discharge is difficult to achieve for Kr and Ar under the operating conditions optimal for Xe. While increasing the discharge voltage can effectively improve the stability of discharge for Kr and Ar, other common strategies such as changing the magnetic field strength, propellant flow rate, and cathode current are not successful. The propellant utilization efficiency and discharge efficiency are affected by both discharge voltage and propellant flow rate. A maximum utilization efficiency and an optimal discharge efficiency are observed for all three propellants, with the values decreasing in the order of Xe, Kr, and Ar. Moreover, the discharge voltage corresponding to the optimal efficiency is inversely proportional to the square root of the propellant mass, indicating that the ion diffusional loss to the wall, rather than the ionization energy, is the dominant factor affecting the discharge performance for alternative propellants in a miniature DC thruster.

Particle-based modeling and GPU-accelerated simulation of cellular blood flow

Computational modeling and simulation of cellular blood flow is highly desired for understanding blood microcirculation and blood-related diseases such as thrombosis and tumor, but it remains a challenging task primarily because blood in microvessels should be described as a dense suspension of different types of deformable cells. The focus of the present work is on the development of a particle-based and GPU-accelerated numerical method that is able to quickly simulate the various behaviors of deformable cells in three-dimensional arbitrarily complex geometries. We employ a two-fluid model to describe blood flow, incorporating the deformation and aggregation of cells. A smoothed dissipative particle dynamics is used to solve the two-fluid model, and a discrete microstructure model is applied for the cell deformation, as well as a Morse potential model for the cell aggregation. The heterogeneous CPU-GPU environment is established, where each GPU thread is dedicated to a particle, and the CPU is mainly responsible for loading and exporting data. Five test cases are conducted against analytical theory, experimental data, and previous numerical results, for pure fluid, cell deformation, cell aggregation, cell suspension and the cellular flow in a complex network, respectively. It is shown that the methodology can accurately predict various behaviors of cells, and the GPU is well suited for particle-based modeling. Especially for cellular blood flow, where calculating cellular forces is a compute-intensive and time-consuming task, the GPU offers exceptional parallel capabilities, significantly enhancing the simulation efficiency. The speedup is about 3.5 times faster than the CPU parallelization with 96 cores for the pure fluid, and this acceleration nearly reaches 20 times when cells are included in the simulations. Particularly, the calculations for deformation and aggregation forces demonstrate a substantial speedup, achieving the improvements of up to 120 and 640 times, respectively, compared to their serial counterparts. The present methodology can effectively integrate various behaviors of cells, and has the potential in simulating very large microvascular networks at organ levels.

Modelling the complete life cycle of an arctic copepod reveals complex trade-offs between concurrent life cycle strategies

Calanus hyperboreus is a large-bodied, biomass dominant species that performs a crucial ecosystem energy transfer by converting the spring phytoplankton bloom into lipid reserves that fuel the higher trophic levels of the Gulf of St. Lawrence (GSL) pelagic ecosystem, including the critically endangered North Atlantic right whale (Eubalena glacialis). Given that the GSL, the southernmost core habitat of C. hyperboreus, is undergoing rapid warming, developing a population model allows us to synthesize existing knowledge of the species, and to examine the species response to environmental conditions. To simulate the multi-year life cycle in the northwest GSL, model equations are implemented for ingestion, assimilation, respiration, egg production, stage development, mortality, and vertical migration behaviors including dormancy entry and exit. The 1-D particle-based model predicts the evolution of individual stage, structural mass, lipid, age, sex, abundance, and egg production, as well as the seasonal evolution of the population structure in the northwest GSL. Individual lipid-based thresholds inform the timing of ontogenetic vertical migration. Life cycle targets defined from a literature review are used to guide model parameterization and assess its performance. The simulated population structure, phenology, and size at stage are generally consistent with observations. Under 10 years of repeat year forcing, the model simulates a quasi-stable overwintering population composed of late stages CIV, CV and CVI. Observations suggest that stage CIV is the first overwintering stage in the GSL, and point to the occurrence of iteroparous females. Using the model, the relative success of diverse dormancy and reproductive phenotypes are explored. Second reproduction females reproduce earlier in winter than first reproduction females, with implications for the ability of the new generation to match the spring bloom and accumulate sufficient lipid to overwinter as stage CIV. Without iteroparity, the time window of reproduction contracts and the population is reduced, underscoring the role of a flexible multi-year life cycle in population success.

A Particle-Based Numerical Model for Impact-Induced Bonding in Cold Spray

A Particle-Based Numerical Model for Impact-Induced Bonding in Cold Spray

Multiscale Modeling of Bistability in the Yeast Polarity Circuit.

Cell polarity refers to the asymmetric distribution of proteins and other molecules along a specified axis within a cell. Polarity establishment is the first step in many cellular processes. For example, directed growth or migration requires the formation of a cell front and back. In many cases, polarity occurs in the absence of spatial cues. That is, the cell undergoes symmetry breaking. Understanding the molecular mechanisms that allow cells to break symmetry and polarize requires computational models that span multiple spatial and temporal scales. Here, we apply a multiscale modeling approach to examine the polarity circuit of yeast. In addition to symmetry breaking, experiments revealed two key features of the yeast polarity circuit: bistability and rapid dismantling of the polarity site following a loss of signal. We used modeling based on ordinary differential equations (ODEs) to investigate mechanisms that generate these behaviors. Our analysis revealed that a model involving positive and negative feedback acting on different time scales captured both features. We then extend our ODE model into a coarse-grained reaction-diffusion equation (RDE) model to capture the spatial profiles of polarity factors. After establishing that the coarse-grained RDE model qualitatively captures key features of the polarity circuit, we expand it to more accurately capture the biochemical reactions involved in the system. We convert the expanded model to a particle-based model that resolves individual molecules and captures fluctuations that arise from the stochastic nature of biochemical reactions. Our models assume that negative regulation results from negative feedback. However, experimental observations do not rule out the possibility that negative regulation occurs through an incoherent feedforward loop. Therefore, we conclude by using our RDE model to suggest how negative feedback might be distinguished from incoherent feedforward regulation.

The application and algorithm of fabric simulation in game industry

fabric simulation has emerged as a crucial aspect of computer graphics, particularly in applications such as film production, video games, and design. This paper provides a comprehensive review of fabric simulation methodologies, implementation techniques, and its diverse applications within the game industry. The fabric simulation algorithms are broken down into Fabric Modeling, Dynamic Simulation, and Collision Handling. The three common modeling strategies of Elasticity-Based Models, Particle-Based Models, and Mass-Spring Damper Models, are discussed in detail. Additionally, dynamic simulation strategies such as Position-Based Dynamics (PBD) and Extended Position-Based Dynamics (XPBD) are explored for their role in real-time simulations, particularly in the game industry. The paper also delves into collision handling algorithms, exploring the strategies for accurate collision detection and response. The application of fabric simulation in the game industry is highlighted, showcasing how it enhances realism and immersion by simulating fabric movement and interactions with the environment. The paper introduced various fabric simulation tools and systems within popular game engines like Unity 3D and Unreal Engine 5. Furthermore, optimization strategies, such as view-dependent adaptive simulation, are discussed to improve performance providing insight for future work.

The application and algorithm of fabric simulation in game industry

fabric simulation has emerged as a crucial aspect of computer graphics, particularly in applications such as film production, video games, and design. This paper provides a comprehensive review of fabric simulation methodologies, implementation techniques, and its diverse applications within the game industry. The fabric simulation algorithms are broken down into Fabric Modeling, Dynamic Simulation, and Collision Handling. The three common modeling strategies of Elasticity-Based Models, Particle-Based Models, and Mass-Spring Damper Models, are discussed in detail. Additionally, dynamic simulation strategies such as Position-Based Dynamics (PBD) and Extended Position-Based Dynamics (XPBD) are explored for their role in real-time simulations, particularly in the game industry. The paper also delves into collision handling algorithms, exploring the strategies for accurate collision detection and response. The application of fabric simulation in the game industry is highlighted, showcasing how it enhances realism and immersion by simulating fabric movement and interactions with the environment. The paper introduced various fabric simulation tools and systems within popular game engines like Unity 3D and Unreal Engine 5. Furthermore, optimization strategies, such as view-dependent adaptive simulation, are discussed to improve performance providing insight for future work.

Particle-based modelling of laser powder bed fusion of metals with emphasis on the melting mode transition

The laser-beam powder bed fusion process for metals, commonly abbreviated as PBF-LB/M, is a widely used process for the additive manufacturing of parts. Numerical simulations are useful to identify optimal process parameters for different materials and to obtain detailed insights into process dynamics. The present work uses a single-phase incompressible Smoothed Particle Hydrodynamics (SPH) scheme to model PBF-LB/M which was found to reduce the required computational time and significantly stabilize the partially violent flow in the melt pool in comparison to a weakly compressible SPH approach. The laser-material interaction is realistically modelled by means of a ray tracing method. An approach to model the effective thermal coductivity of the powder bed is proposed. Excellent agreement between the simulation results and experimental X-ray analyses of the transition from conduction melting mode to keyhole mode including geometric properties of the vapor depression zone was found. These results prove the usability of SPH as a high precision simulation tool for PBF-LB/M.Graphic abstract

OPTIMIZATION-DRIVEN STATISTICAL MODELS OF ANATOMIES USING RADIAL BASIS FUNCTION SHAPE REPRESENTATION.

Particle-based shape modeling (PSM) is a popular approach to automatically quantify shape variability in populations of anatomies. The PSM family of methods employs optimization to automatically populate a dense set of corresponding particles (as pseudo landmarks) on 3D surfaces to allow subsequent shape analysis. A recent deep learning approach leverages implicit radial basis function representations of shapes to better adapt to the underlying complex geometry of anatomies. Here, we propose an adaptation of this method using a traditional optimization approach that allows more precise control over the desired characteristics of models by leveraging both an eigenshape and a correspondence loss. Furthermore, the proposed approach avoids using a black-box model and allows more freedom for particles to navigate the underlying surfaces, yielding more informative statistical models. We demonstrate the efficacy of the proposed approach to state-of-the-art methods on two real datasets and justify our choice of losses empirically.

Colloid Transport in Bicontinuous Nanoporous Media.

Colloid transport and retention in porous media are critical processes influencing various Earth science applications, from groundwater remediation to enhanced oil recovery. These phenomena become particularly complex in the confined spaces of nanoporous media, where strong boundary layer effects and nanoconfinement significantly alter colloid behavior. In this work, we use particle dynamics models to simulate colloid transport and retention processes in bicontinuous nanoporous (BNP) media under pressure gradients. By utilizing particle-based models, we track the movement of each colloid and elucidate the underlying colloid retention mechanisms. Under unfavorable attachment conditions, the results reveal two colloid retention mechanisms: physical straining and trapping in low-flow zone. Furthermore, we investigate the effects of critical factors including colloid volume fraction, d, pressure difference, ΔP, interaction between colloids and BNP media, Ec-p, and among colloids, Ec-c, on colloid transport. Analysis of breakthrough curves and colloid displacements demonstrates that higher values of d, lower values of ΔP, and strong Ec-p attractions significantly increase colloid retention, which further lead to colloid clogging and jamming. In contrast, Ec-c has minimal impact on colloid transport due to the limited colloid-colloid interaction in nanoporous channels. This work provides critical insights into the fundamental factors governing colloid transport and retention within stochastic nanoporous materials.

Dissipative particle dynamics for coarse-grained models.

We develop a computational method based on Dissipative Particle Dynamics (DPD) that introduces solvent hydrodynamic interactions to coarse-grained models of solutes, such as ions, molecules, or polymers. DPD-solvent (DPDS) is a fully off-lattice method that allows straightforward incorporation of hydrodynamics at desired solvent viscosity, compressibility, and solute diffusivity with any particle-based solute model. Solutes interact with the solvent only through the DPD thermostat, which ensures that the equilibrium properties of the solute system are not affected by the introduction of the DPD solvent, while the thermostat coupling strength sets the desired solute diffusivity. Thus, DPDS can be used as a replacement for traditional molecular dynamics thermostats such as Nosé-Hoover and Langevin. We demonstrate the applicability of DPDS in the case of polymer dynamics and electroosmotic flow through a nanopore. The method should be broadly useful as a means to introduce hydrodynamic interactions to existing coarse-grained models of solutes and soft materials.

Discrete modeling of elastic heterogeneous media

Discrete models provide advantages in simulating fracture in quasi-brittle materials due, in part, to their simplicity in representing cracking and other forms of displacement discontinuity. However, the stress analyses that form the basis for fracture simulation are complicated by difficulties in modeling the Poisson effect and other aspects of elastic behavior. The capabilities of Voronoi-cell lattice models, which are a form of particle-based lattice model, for elastic stress analysis are evaluated. It is found that the conventional means for representing the Poisson effect in particle-based lattice models result in spatially correlated stress oscillations that, at first glance, mimic the effects of material heterogeneity. The correlation length is dependent on discretization size. Alternatively, material heterogeneity can be introduced into elastically uniform lattice models via random assignments of material properties, independent of mesh size and geometry.

Experimental and numerical study of conductive heat transfer in aggregate particles using a flighted, rotary drum

Heating aggregates and recycled materials is an energy intensive part of the process of producing asphalt pavement. Sustainability can be improved by increasing the portion of recycled materials while decreasing the energy required to produce the asphalt mixture. A common method to incorporate recycled materials is to mix heated aggregate with cold recycled materials. Interstitial gas conduction is the dominant mode of heat transfer in this process due to the low thermal conductivity of these materials. In this study, a particle-based model that includes the contribution of the interstitial gases was validated with a novel experimental method using infrared (IR) thermography to track the temperature evolution of a mixture of hot and cold aggregates in a flighted, inclined drum. The simulation results showed good agreement with the experiment demonstrating the model’s ability to capture the heat transfer and the particle flow in the dynamic system of irregular shaped particles.

Simulating the rheology of dense suspensions using pairwise formulation of contact, lubrication and Brownian forces

Dense suspensions of solid particles in viscous liquid are ubiquitous in both industry and nature, and there is a clear need for efficient numerical routines to simulate their rheology and microstructure. Particles of micron size present a particular challenge: at low shear rates, colloidal interactions control their dynamics while at high rates, granular-like contacts dominate. While there are established particle-based simulation schemes for large-scale non-Brownian suspensions using only pairwise lubrication and contact forces, common schemes for colloidal suspensions generally are more computationally costly and thus restricted to relatively small system sizes. Here, we present a minimal particle-based numerical model for dense colloidal suspensions that incorporates Brownian forces in pairwise form alongside contact and lubrication forces. We show that this scheme reproduces key features of dense suspension rheology near the colloidal-to-granular transition, including both shear thinning due to entropic forces at low rates and shear thickening at high rates due to contact formation. This scheme is implemented in LAMMPS, a widely used open source code for parallelised particle-based simulations, with a runtime that scales linearly with the number of particles, making it amenable for large-scale simulations.

A general purpose parallel Fortran code for grid projected concentration reconstruction from multidimensional particle distributions

Particle-based transport models are widely used for simulating the movement of pollutants in environmental systems. Unlike grid-based methods, particles naturally address advective transport without numerical dispersion. However, these models require concentration reconstruction from the discrete particle information. This is computationally demanding in multidimensional problems, posing a challenge for field-scale models requiring frequent reconstruction. Grid Projected Kernel Density Estimation (GPKDE) is a cell-averaged reconstruction with improvements in computational performance compared to classical KDE. Currently, no programs implementing this method are readily integrated into particle simulators, compatible with three-dimensional domains, and particles with unequal weights. This article introduces a Fortran code for general-purpose GPKDE, with modular functionalities facilitating the integration into external software. The program implements locally adaptive kernel bandwidth selection and alternatives for the reconstruction from particles with non-uniform weights. The code is parallelized with the OpenMP library. Numerical test cases demonstrate the program’s applicability and scalability of the parallel implementation.