Particle-based Model Research Articles

Smoothed Particle Hydrodynamics simulations of reef surf zone processes driven by plunging irregular waves

As waves interact with the slopes of coral reefs and other steep bathymetry profiles, plunging breaking usually occurs where the free surface overturns and violent water motion is triggered. Resolving these surf zone processes pose significant challenges for conventional mesh-based hydrodynamic models, due to the rapidly-deforming nature of the free surface and associated flows. Yet the accurate prediction of these surf zone hydrodynamics is critical for predicting a wide range of nearshore processes driven by wave breaking (e.g., wave dissipation and energy transfers; mean water levels and currents; and wave runup). In this study we assess the ability of the mesh-free, Lagrangian particle-based numerical modelling approach Smoothed Particle Hydrodynamics (SPH) based on DualSPHysics, to simulate the fine-scale hydrodynamic processes driven by irregular wave transformation over a fringing reef profile, by comparing results against detailed experimental observations from a physical modelling study. To greatly improve the computational efficiency, the SPH model was coupled to the mesh-based multi-layer nonhydrostatic wave-flow model SWASH. With this coupled approach, SWASH was used to efficiently simulate the evolution of non-breaking waves from the wavemaker up to the fore reef slope, with the SPH model then used to simulate the detailed hydrodynamic processes over the reef from just offshore of the breakpoint to the shoreline. The SPH model was able to accurately reproduce the complex free surface deformations during plunging breaking, the spectral evolution of waves across the reef flat (including nonlinear wave shape), the mean water levels and currents, and wave runup at the shoreline. Using the long duration simulations (>400 wave periods), the model was able to reproduce the full range of wave motions over the reef (from sea-swell to infragravity frequencies), including the increasing dominance of low frequency waves towards the shoreline and the large cross-reef standing wave motions excited by the reef geometry.

Coarse-grained models for local density gradients

Coarse-grained (CG) models provide superior computational efficiency for simulating soft materials. Unfortunately, CG models with conventional pair-additive potentials demonstrate limited transferability between bulk and interfacial environments. Recently, a growing number of CG models have supplemented these pair potentials with one-body potentials of the local density (LD) around each site. These LD potentials can significantly improve the accuracy and transferability of CG models. Nevertheless, it remains challenging to accurately describe interfaces where the LD varies rapidly. In this work, we consider a new class of one-body potentials that depend upon the square of the LD gradient around each site. We investigate the impact of this square gradient (SG) potential upon both top-down dissipative particle dynamics (DPD) models and also bottom-up multiscale coarse-graining (MS-CG) models. We demonstrate that SG potentials can be used to tune the interfacial properties of DPD models without significantly altering their bulk properties. Moreover, we demonstrate that SG potentials can improve the bulk pressure-density equation of state as well as the interfacial profile of MS-CG models for acetic acid. Consequently, SG potentials may provide a useful connection between particle-based top-down models and mean-field Landau theories for phase behavior. Furthermore, SG potentials may prove useful for improving the accuracy and transferability of bottom-up CG models for interfaces and other inhomogeneous systems with significant density gradients.

Experimental validation of a particle-based method for heat transfer incorporating interstitial gas conduction in dense granular flow using a rotary drum

Conductive heat transfer in granular material is important in many industrial processes. For dense systems where the materials have low thermal conductivity, much of the heat transfer will occur through interstitial gases. Industry requires flexible and efficient computational methods to capture these phenomena at scale. In this study, a recently proposed particle-based model that includes the contribution of the interstitial gases was validated using an experiment. This model was originally derived from a multi-scale analysis of static, random packings. To test this approach in dense, dynamic systems, the model results were compared to experimental data for glass beads in an indirectly heated rotating drum. Infrared (IR) thermography was used to track the temperature evolution of the glass beads and the drum wall temperature. Discrete element simulations were performed with the experimental wall temperature used as a transient wall boundary condition. Results from the simulation show good agreement with the experimental data both for the bulk average temperature and for the bed profile, demonstrating the model’s ability to capture the gas contribution in dynamic systems.

Computational investigation of blood cell transport in retinal microaneurysms.

Microaneurysms (MAs) are one of the earliest clinically visible signs of diabetic retinopathy (DR). MA leakage or rupture may precipitate local pathology in the surrounding neural retina that impacts visual function. Thrombosis in MAs may affect their turnover time, an indicator associated with visual and anatomic outcomes in the diabetic eyes. In this work, we perform computational modeling of blood flow in microchannels containing various MAs to investigate the pathologies of MAs in DR. The particle-based model employed in this study can explicitly represent red blood cells (RBCs) and platelets as well as their interaction in the blood flow, a process that is very difficult to observe in vivo. Our simulations illustrate that while the main blood flow from the parent vessels can perfuse the entire lumen of MAs with small body-to-neck ratio (BNR), it can only perfuse part of the lumen in MAs with large BNR, particularly at a low hematocrit level, leading to possible hypoxic conditions inside MAs. We also quantify the impacts of the size of MAs, blood flow velocity, hematocrit and RBC stiffness and adhesion on the likelihood of platelets entering MAs as well as their residence time inside, two factors that are thought to be associated with thrombus formation in MAs. Our results show that enlarged MA size, increased blood velocity and hematocrit in the parent vessel of MAs as well as the RBC-RBC adhesion promote the migration of platelets into MAs and also prolong their residence time, thereby increasing the propensity of thrombosis within MAs. Overall, our work suggests that computational simulations using particle-based models can help to understand the microvascular pathology pertaining to MAs in DR and provide insights to stimulate and steer new experimental and computational studies in this area.

Wetting dynamics under periodic switching on different scales: characterization and mechanisms.

The development of substrates with a switchable wettability is proceeding and the limit of switching frequencies and contact angle differences between substrate states has developed in the past years. In this paper we investigate the behavior of a droplet on a homogeneous substrate, which is switched between two wettabilities for a large range of switching frequencies. Here, we are particularly interested in the dependence of the wetting behavior on the switching frequency. We show that results obtained on the particle level via molecular dynamics simulations and on the continuum level via the thin-film model are consistent. Predictions of simple models as the molecular theory of wetting (MKT) and analytical calculations based on the MKT also show good agreement and offer deeper insights into the underlying mechanisms.

Investigating the entropic nature of membrane-mediated interactions driving the aggregation of peripheral proteins.

Peripheral membrane-associated proteins are known to accumulate on the surface of biomembranes as a result of membrane-mediated interactions. For a pair of rotationally-symmetric curvature-inducing proteins, membrane mechanics at the low-temperature limit predicts pure repulsion. On the other hand, temperature-dependent entropic forces arise between pairs of stiff-binding proteins suppressing membrane fluctuations. These Casimir-like interactions have thus been suggested as candidates for attractive forces leading to aggregation. With dense assemblies of peripheral proteins on the membrane, both these abstractions encounter short-range and multi-body complications. Here, we make use of a particle-based membrane model augmented with flexible peripheral proteins to quantify purely membrane-mediated interactions and investigate their underlying nature. We introduce a continuous reaction coordinate corresponding to the progression of protein aggregation. We obtain free energy and entropy landscapes for different surface concentrations along this reaction coordinate. In parallel, we investigate time-dependent estimates of membrane entropy corresponding to membrane undulations and coarse-grained director field and how they change dynamically with protein aggregation. Congruent outcomes of the two approaches point to the conclusion that for low surface concentrations, interactions with an entropic nature may drive the aggregation. But at high concentrations, enthalpic contributions due to concerted membrane deformation by protein clusters are dominant.

Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach.

Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectional study designs and hence has limited statistical power in representing shape changes over time. Existing methods for modeling spatiotemporal or longitudinal shape changes require predefined shape atlases and pre-built shape models that are typically constructed cross-sectionally. This paper proposes a data-driven approach inspired by the PSM method to learn population-level spatiotemporal shape changes directly from shape data. We introduce a novel SSM optimization scheme that produces landmarks that are in correspondence both across the population (inter-subject) and across time-series (intra-subject). We apply the proposed method to 4D cardiac data from atrial-fibrillation patients and demonstrate its efficacy in representing the dynamic change of the left atrium. Furthermore, we show that our method outperforms an image-based approach for spatiotemporal SSM with respect to a generative time-series model, the Linear Dynamical System (LDS). LDS fit using a spatiotemporal shape model optimized via our approach provides better generalization and specificity, indicating it accurately captures the underlying time-dependency.

Particle-Based Numerical Modelling of Liquid Marbles: Recent Advances and Future Perspectives

Particle-Based Numerical Modelling of Liquid Marbles: Recent Advances and Future Perspectives

Attractive internuclear force drives the collective behavior of nuclear arrays in Drosophila embryos.

The collective behavior of the nuclear array in Drosophila embryos during nuclear cycle (NC) 11 to NC14 is crucial in controlling cell size, establishing developmental patterns, and coordinating morphogenesis. After live imaging on Drosophila embryos with light sheet microscopy, we extract the nuclear trajectory, speed, and internuclear distance with an automatic nuclear tracing method. We find that the nuclear speed shows a period of standing waves along the anterior-posterior (AP) axis after each metaphase as the nuclei collectively migrate towards the embryo poles and partially move back. And the maximum nuclear speed dampens by 28-45% in the second half of the standing wave. Moreover, the nuclear density is 22–42% lower in the pole region than the middle of the embryo during the interphase of NC12-14. To find mechanical rules controlling the collective motion and packing patterns of the nuclear array, we use a deep neural network (DNN) to learn the underlying force field from data. We apply the learned spatiotemporal attractive force field in the simulations with a particle-based model. And the simulations recapitulate nearly all the observed characteristic collective behaviors of nuclear arrays in Drosophila embryos.

A force-driven model for passenger evacuation in bus fires

A generic evacuation model is developed based on the behavioral characteristics of passengers during bus fires. Inspired by hydrodynamic mechanisms, the proposed particle-based force-driven model is built using force analysis of evacuees and analogs the movement of people as fluid motion. Several modules are developed to characterize evacuees’ preferences for varying exits, aggregated behavior of groups, and effects of driver. The Smoothed Particle Hydrodynamics (SPH) methods are applied to solve the proposed model using smoothed kernel functions. Experiments are conducted to calibrate and validate the proposed model based on observed evacuees’ behavioral characteristics, such as evacuation time, evacuation speed under different scenarios in terms of bus capacity, and the interaction between evacuees. The results of the study will contribute to a better understanding of bus evacuation options, improved design of bus vehicles to cope with internal fires, and the development of appropriate policies and regulations for bus passenger evacuation in response to fires. The proposed method can also be readily applied to other related fields with minor modifications.

Influence of thermal fluctuations on nanoscale free-surface flows: A many-body dissipative particle dynamics study

Free-surface fluid dynamics at the nanoscale is shown to strongly depend on thermal fluctuations. These fluctuations can be predicted by particle-based models numerically and analyzed by stochastic partial differential equations theoretically. In this work, we employ one of the particle methods, known as many-body dissipative particle dynamics (mDPD), to investigate the influence of thermal fluctuations on nanoscale free-surface flows. After enough independent simulations are performed for convincing ensemble-averaged results, mDPD not only shows good consistency with theories derived from the Landau–Lifshitz Navier–Stokes equations, but also indicates high efficiency in contrast with traditional molecular dynamics (MD). These results confirm that mDPD has capabilities of capturing the physics of fluctuating hydrodynamics in nanoscale free-surface flows, especially the complicated three-dimensional behaviors that cannot be predicted by theoretical models, both accurately and efficiently.

Fatigue and micro-seismic behaviors of concrete disks exposed to cyclic Brazilian testing: A numerical investigation based on a 3D particle-based model

This article aims to numerically investigate the mechanical behaviour of concrete Brazilian Disk (BD) samples when exposed to multi-level cyclic Brazilian loading. A numerical 3D particle-based model was successfully established to simulate the dynamic stress-controlled cyclic testing. A generalized model is proposed based on the time-dependent evolution of bond diameter. This model shows great effectiveness in terms of reproducing the stress-strain curves, axial strain evolution and displacement fields as observed during lab testing. An acoustic emission (AE) tracking algorithm was implemented in the model to characterize and visualize the released AE energy connected with micro fracturing. The AE energy is found to be stress-level dependent which is consistent with lab testing. The failure pattern in numerical simulation is also in line with lab results. The distinctions between cyclic compressive and cyclic Brazilian tests are discussed and summarized.

Shear behaviors of natural rock fractures infilled with cemented calcite

Shear behaviors of naturally infilled granite fractures were investigated using experimental and numerical approaches in this study. The tested granite fracture samples were collected from two boreholes BS48 and BS49 at the Xinchang site in the Beishan area of Gansu Province, China. Particle-based DEM models for the naturally infilled granite fractures were built up and calibrated with the experimental results. Effects of normal stress and infilling ratio (t/a) on shear behaviors and properties of infilled fractures were investigated. The results show that the shear stress-shear displacement curves of the naturally infilled fractures include three shear phases, i.e., quasi-elastic phase, failure phase, and residual shear phase. The relationships between normal stresses and peak shear strength, residual shear strength, shear stiffness and peak dilation are fitted using an approximately linear function. Infilling ratio (t/a) has a significant influence on the shear properties of the naturally infilled fractures. Shear strength and stiffness of the naturally infilled fractures decrease with increasing (t/a). The different shear behaviors of the various infilled and clean fractures are also briefly compared. Practical implications and outstanding issues in the present study are discussed finally.

Curvature flows, scaling laws and the geometry of attrition under impacts

Impact induced attrition processes are, beyond being essential models of industrial ore processing, broadly regarded as the key to decipher the provenance of sedimentary particles. Here we establish the first link between microscopic, particle-based models and the mean field theory for these processes. Based on realistic computer simulations of particle-wall collision sequences we first identify the well-known damage and fragmentation energy phases, then we show that the former is split into the abrasion phase with infinite sample lifetime (analogous to Sternberg’s Law) at finite asymptotic mass and the cleavage phase with finite sample lifetime, decreasing as a power law of the impact velocity (analogous to Basquin’s Law). This splitting establishes the link between mean field models (curvature-driven partial differential equations) and particle-based models: only in the abrasion phase does shape evolution emerging in the latter reproduce with startling accuracy the spatio-temporal patterns (two geometric phases) predicted by the former.

Analysis of microvascular thrombus mechanobiology with a novel particle-based model

Platelet accumulation at the site of a vascular injury is regulated by soluble platelet agonists, which induce various types of platelet responses, including integrin activation and granule secretion. The interplay between local biochemical cues, mechanical interactions between platelets and macroscopic thrombus dynamics is poorly understood.Here we describe a novel computational model of microvascular clot formation for the detailed analysis of thrombus mechanics. We adopt a previously developed two-dimensional particle-based model focused on the thrombus shell formation and revise it to introduce the platelet agonists. Blood flow is simulated via a computational fluid dynamics approach. In order to model soluble platelet activators, we apply Langevin dynamics to a large number of non-dimensional virtual particles. Taking advantage of the available data on platelet dense granule secretion kinetics, we model platelet degranulation as a stochastic agonist-dependent process.The new model qualitatively reproduces the enhanced thrombus formation due to dense granule secretion, in line with in vivo findings, and provides a mechanism for the thrombin confinement at the early stages of clot formation. Our calculations also predict that the release of platelet dense granules results in the additional mechanical stabilization of the inner layers of thrombus. Distribution of the inter-platelet forces throughout the aggregate reveals multiple weak spots in the outer regions of a thrombus, which are expected to result in the mechanical disruptions at the later stages of clot formation.

Discrete mechanical models of concrete fracture

Discrete models of solids have been motivated, in large part, by the discontinuous and heterogeneous nature of material structure and its breakdown under loading. The capabilities of discrete models have evolved over the past several decades, offering novel means for investigating material structure–property relationships. However, lack of understanding of both the utilities and disadvantages of discrete models limits their further development and applications. This paper reviews relevant features of discrete approaches applied to modeling the mechanical behavior of geomaterials, concrete materials in particular. The discrete models are classified according to their form and abilities to represent elastic and fracture behaviors in the presence of large-scale material heterogeneity. Discretization of the material domain plays a large role in this respect. Emphasis is placed on particle-based lattice models. The relative merits of various strategies for introducing reinforcing components, which are essential for many applications, are outlined. Recent advances are highlighted, including the use of discrete models for coupled, multi-field analysis. The merits of discrete approaches are summarized in the conclusions.

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Since the classical molecular dynamics simulator LAMMPS was released as an open source code in 2004, it has become a widely-used tool for particle-based modeling of materials at length scales ranging from atomic to mesoscale to continuum. Reasons for its popularity are that it provides a wide variety of particle interaction models for different materials, that it runs on any platform from a single CPU core to the largest supercomputers with accelerators, and that it gives users control over simulation details, either via the input script or by adding code for new interatomic potentials, constraints, diagnostics, or other features needed for their models. As a result, hundreds of people have contributed new capabilities to LAMMPS and it has grown from fifty thousand lines of code in 2004 to a million lines today. In this paper several of the fundamental algorithms used in LAMMPS are described along with the design strategies which have made it flexible for both users and developers. We also highlight some capabilities recently added to the code which were enabled by this flexibility, including dynamic load balancing, on-the-fly visualization, magnetic spin dynamics models, and quantum-accuracy machine learning interatomic potentials. Program SummaryProgram Title: Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)CPC Library link to program files:https://doi.org/10.17632/cxbxs9btsv.1Developer's repository link:https://github.com/lammps/lammpsLicensing provisions: GPLv2Programming language: C++, Python, C, FortranSupplementary material:https://www.lammps.orgNature of problem: Many science applications in physics, chemistry, materials science, and related fields require parallel, scalable, and efficient generation of long, stable classical particle dynamics trajectories. Within this common problem definition, there lies a great diversity of use cases, distinguished by different particle interaction models, external constraints, as well as timescales and lengthscales ranging from atomic to mesoscale to macroscopic.Solution method: The LAMMPS code uses parallel spatial decomposition, distributed neighbor lists, and parallel FFTs for long-range Coulombic interactions [1]. The time integration algorithm is based on the Størmer-Verlet symplectic integrator [2], which provides better stability than higher-order non-symplectic methods. In addition, LAMMPS supports a wide range of interatomic potentials, constraints, diagnostics, software interfaces, and pre- and post-processing features.Additional comments including restrictions and unusual features: This paper serves as the definitive reference for the LAMMPS code.

Hydrodynamic coupling for particle-based solvent-free membrane models.

The great challenge with biological membrane systems is the wide range of scales involved, from nanometers and picoseconds for individual lipids to the micrometers and beyond millisecond for cellular signaling processes. While solvent-free coarse-grained membrane models are convenient for large-scale simulations and promising to provide insight into slow processes involving membranes, these models usually have unrealistic kinetics. One major obstacle is the lack of an equally convenient way of introducing hydrodynamic coupling without significantly increasing the computational cost of the model. To address this, we introduce a framework based on anisotropic Langevin dynamics, for which major in-plane and out-of-plane hydrodynamic effects are modeled via friction and diffusion tensors from analytical or semi-analytical solutions to Stokes hydrodynamic equations. Using this framework, in conjunction with our recently developed membrane model, we obtain accurate dispersion relations for planar membrane patches, both free-standing and in the vicinity of a wall. We briefly discuss how non-equilibrium dynamics is affected by hydrodynamic interactions. We also measure the surface viscosity of the model membrane and discuss the affecting dissipative mechanisms.

Particle-based model captures fine details of complete droplet freezing process

Particle-based simulations incorporate freezing characteristics irreplicable in mesh-based methods, reproducing the complete two-phase, four-stage icing process in mesoscopic droplets.

Skyrmion ratchet in funnel geometries

Using a particle-based model, we simulate the behavior of a skyrmion under the influence of asymmetric funnel geometries and ac driving at zero temperature. We specifically investigate possibilities for controlling the skyrmion motion by harnessing a ratchet effect. Our results show that as the amplitude of a unidirectional ac drive is increased, the skyrmion can be set into motion along either the easy or hard direction of the funnel depending on the ac driving direction. When the ac drive is parallel to the funnel axis, the skyrmion flows in the easy direction and its average velocity is quantized. In contrast, when the ac drive is perpendicular to the funnel axis, a Magnus-induced ratchet effect occurs, and the skyrmion moves along the hard direction with a constant average velocity. For biharmonic ac driving of equal amplitude along both the parallel and perpendicular directions, we observe a reentrant pinning phase where the skyrmion ratchet vanishes. For asymmetric biharmonic ac drives, the skyrmion exhibits a combination of effects and can move in either the easy or hard direction depending on the configuration of the ac drives. These results indicate that it is possible to achieve controlled skyrmion motion using funnel geometries, and we discuss ways in which this could be harnessed to perform data transfer operations.