Particle-based Simulation Research Articles

Development and experimental validation of a texture-based 3D liberation model

Prediction of mineral liberation is one of the key steps in establishing a link between ore texture and its processing behavior. With the rapid development of X-ray Microcomputed Tomography (µCT), the extension of liberation modeling into 3D realms becomes possible. Liberation modeling allows for the generation of particle population from 3D texture data in a completely non-destructive manner. This study presents a novel texture-based 3D liberation model that is capable of predicting liberation from 3D drill core image acquired by µCT. The model takes preferential, phase-boundary, and random breakage into account with differing relative contributions to the liberation depending on the ore texture itself. The model was calibrated using experimental liberation data measured in 3D µCT. After calibration, the liberation model was found to be capable of explaining on average of around 84% of the variance in the experimental liberation data. The generated particle population can be used for particle-based process simulation to evaluate the process responses of various ore textures subjected to various modes of breakage.

The dynamics of microplastics and associated contaminants: Data-driven Lagrangian and Eulerian modelling approaches in the Mediterranean Sea.

Plastic pollution is widespread in the global oceans, but at the same time several other types of hydrophobic pollutants contaminate the marine environment. As more and more evidence highlights, microplastics and polluting chemicals are intertwined via adsorption/desorption processes. A thorough assessment of their total impact on marine ecosystems thus requires that these two kinds of pollution are not considered separately. Here we compare the outcomes of two complementary, data-driven modelling approaches for microplastic dispersal and for Plastic-Related Organic Pollutants (PROPs) in the marine environment. Focusing on the Mediterranean Sea, we simulate two years of Lagrangian particle tracking to map microplastic dispersion from the most impacting sources of pollution (i.e. coastal areas, the watersheds of major rivers, and fishing activities). Our particle sources are data-informed by national census data, hydrological regimes, and vessel tracking data to account for spatial and temporal variability of mismanaged plastic waste generation. These particle-based simulations are complemented with a simulation of the dynamics of primary pollutants in the sea, obtained via an advection-diffusion Eulerian model. While providing further understanding of the spatiotemporal distribution of microplastics and the dynamics of PROPs at a Mediterranean-wide scale, our results call for the development of novel integrated modelling approaches aimed at coupling the dynamics of microplastics with the chemical exchanges occurring through them, thus promoting a holistic description of marine plastic pollution.

Modeling of weak polyelectrolyte hydrogels under compression – Implications for water desalination

Hydrogels immersed in salty solutions induce partitioning of small ions that can be exploited for water desalination. This partitioning also affects the H+ ions, leading to a decrease of the pH in the gel as compared to supernatant solution. When using weak polyelectrolyte gels, this lower pH may decrease their ionization degree, and hence diminish their desalination capacity. Using the recently developed grand-reaction ensemble, we performed particle-based computer simulations of a gel in contact with a reservoir of salt solution at pH ≃ 7. We showed that the effect of ion partitioning on the ionization degree is negligible, as long as the salinity of the reservoir is high. However, when the reservoir salinity approaches that of fresh water, the ionization degree of weak polyelectrolyte gel is significantly decreased, thus making it a less efficient desalination agent. Furthermore, we demonstrated that the ionization degree of the gel is decreased upon compression. However, up to pressures of approximately 30 bar this does not significantly affect the ion partitioning. Our result predict that weak polyelectrolyte hydrogels, such as the often employed poly(acrylic acid), can be efficiently used to lower the salinity of sea- or brackish water, while they will be inefficient in desalinating water of low salinity.

Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs

Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs

Machine learning of lubrication correction based on GPR for the coupled DPD-DEM simulation of colloidal suspensions.

Hydrodynamic interactions have a major impact on the suspension properties, but they are absent in atomic and molecular fluids due to a lack of intervening medium at close range. To reproduce the correct hydrodynamic interactions, lubrication correction is essential to compensate the missing short-range hydrodynamics from the fluids. However, lubrication correction requires many simulations in particle-based simulations of colloidal suspensions. To address the problem, we employ an active learning strategy based on Gaussian process regression (GPR) for normal and tangential lubrication corrections to significantly reduce the number of necessary simulations and apply the correction to the coupled multiscale simulation of monodisperse hard-sphere colloidal suspensions. In particular, a single-particle dissipative particle dynamics (DPD) model with parameter correction is used to describe the solvent-solvent and colloid-solvent interactions, and a discrete element method (DEM) model to depict the colloid-colloid frictional contacts. The lubrication correction results demonstrate that only six and four independent simulations (observation points for GPR training) are required to achieve accurate normal and tangential lubrication corrections, respectively. To validate the machine learning of lubrication correction based on GPR, we investigate the self-diffusion coefficients of colloids, suspension rheology and microstructure using the coupled DPD-DEM model with GPR lubrication correction. Our simulation results show that the machine learning of lubrication correction based on GPR is effective and the lubrication corrected DPD-DEM model is indeed capable of accurately capturing hydrodynamic interactions and correctly reproducing dynamical and rheological properties of colloidal suspensions. Moreover, the machine learning of lubrication correction based on GPR is not limited to the coupled DPD-DEM simulation of colloidal suspensions presented here, but can be easily applied to other particle-based simulations of particulate suspensions.

Consititutive modeling of time-dependent flows in frictional suspensions

This paper summarizes recent joint work towards a constitutive modelling framework for dense granular suspensions. The aim is to create a time-dependent, tensorial theory that can implement the physics described in steady state by the Wyart-Cates model. This model of shear thickening suspensions supposes that lubrication films break above a characteristic normal force so that frictional contact forces come into play: the resulting non-sliding constraints can be enough to rigidify a system that would flow freely at lower stresses [1]. Implementing this idea for time-dependent flows requires the introduction of new concepts including a configuration-dependent ‘jamming coordinate’, alongside a decomposition of the velocity gradient tensor into compressive and extensional components which then enter the evolution equation for particle contacts in distinct ways. The resulting approach [2, 3] is qualitatively successful in addressing (i) the collapse of stress during flow reversal in shear flow, and (ii) the ability of transverse oscillatory flows to unjam the system. However there is much work required to refine this approach towards quantitative accuracy, by incorporating more of the physics of contact evolution under flow as determined by close interrogation of particle-based simulations.

Analytical nonlocal model for shear localization in wall-bounded dense granular flow

This work employs a Landau-Ginzburg-type nonlocal rheology model to account for shear localization in a wall-bounded dense granular flow. The configuration is a 3D shear cell in which the bottom bumpy wall moves at a constant speed, while a load pressure is applied at the top bumpy wall, with flat but frictional lateral walls. At a fixed pressure, shear zones transit from the top to the bottom when increasing lateral wall friction coefficient. With a quasi-2D model simplification, asymptotic solutions for fluidization order parameters near the top and bottom boundaries are sought separately. Both solutions are the Airy function in terms of a depth coordinate scaled by a characteristic length which measures the width of the corresponding shear zone. The theoretical predictions for the shear zone widths against lateral wall friction coefficient and load pressure agree well with data extracted from particle-based simulation for the flow.

Toward a large-scale particle-based parallel simulator of Aeolian sand transport, including a model for mobile sand availability

We develop a numerical tool for particle-based simulations of Aeolian sand transport. Our model combines a Discrete-Element-Method for the sand particles with an efficient hydrodynamic description of the average turbulent horizontal wind velocity field over the granular bed, which has been developed in previous work and accounts for the two-way coupling of the granular and fluid phases. However, here we implement our model within the open source library LAMMPS for granular massively parallel simulations and incorporate a new grid coarsening scheme for the wind model. We show that our model quantitatively reproduces observed values of the steady-state (saturated) sand flux under various flow conditions. Furthermore, we model different conditions of mobile sand availability and find a strong dependence of the sand flux on this availability.

Molecular Type Spread Molecular Shift Keying for Multiple-Access Diffusive Molecular Communications

In nano-networking, many nano-machines need to share common communication media, in order to achieve information exchange and data fusion. Multiple-access is an important technique for multiple nano-machines to send information to one access point (AP) or fusion center. Built on the Molecular Shift Keying (MoSK) modulation, this article proposes a novel Molecular Type Spread MoSK (MTS-MoSK) scheme for achieving multiple-access transmission in diffusion-based molecular communications (DMC). Correspondingly, two detection schemes are introduced and investigated, which are the Maximum Selection assisted Majority Voter Detection (MS-MVD) and Equal-Gain Combining Detection (EGCD). The error performance of the MTS-MoSK DMC systems with respectively the two detection schemes is analyzed, when both Multiple-Access Interference (MAI) and Inter-Symbol Interference (ISI) are taken into account. Furthermore, the performance of MTS-MoSK DMC systems is investigated and compared with the aid of analytical results as well as Monte-Carlo and particle-based simulations. Our studies and performance results show that MTS-MoSK constitutes a promising candidate for implementing multiple-access DMC, and MS-MVD has the potential to outperform EGCD in DMC.

Avalanche induced rapid impedance change and electron power absorption during gas breakdown under radio-frequency excitation

During the initiation of a gas discharge under radio-frequency excitation we find that the system undergoes a sequence of distinct electron power absorption modes, while its impedance changes on the nanosecond timescale within the pre-breakdown, breakdown and post-breakdown phases. The experimental results for the spatiotemporal distribution of the excitation rate as well as other plasma parameters during the breakdown process are confirmed by particle-based kinetic simulations. The phenomenon is followed by an analytical model that sheds light on the temporal variation of the current and voltage waveforms as well as their phase difference leading to the rapid variation of the impedance during the build-up of the plasma.

Impacts of Unintended Nanomachine in Diffusion-Based Molecular Communication System

The effect of unintended nanomachine (UN) on the performance of a 3-dimensional (3-D) diffusive point-to-point molecular communication system is investigated in this article. To analyze the impacts of the presence of UN, two different scenarios are considered. In the first scenario, UN is considered as an unintended transmitter nanomachine (UTN). The expression for arrival probability of molecules received at the spherical absorbing receiver, considering exponential molecular degradation, is obtained. Moreover, this scenario is analyzed in terms of average probability of error (APoE) and maximum achievable rate (MAR), with and without Genie-aided assumptions. Further, in the second scenario, wherein we consider UN as a unintended receiver nanomachine (URN), the system is analyzed in terms of information leakage and maximum achievable secrecy rate (MASR). In second scenario, we consider two different models to find the arrival probability of molecules at the intended receiver nanomachine (IRN). In the first model, independence of the particle death in URN and IRN is assumed, and in second model, mutual influences of URN and IRN on each other are assumed. Moreover, in both the scenarios, to optimize the system performance, an optimal detector is used. In the end, the derived analytical expressions are verified through particle-based and Monte-Carlo simulations.

Numerical and experimental study on hydrodynamic performance of multi-level OWEC

The performance of a multi-level overtopping wave energy converter (OWEC) has been numerically and experimentally investigated in a two-dimensional wave tank in order to study the effects of opening width of additional reservoirs. The device is a fixed OWEC consisting of an inclined ramp together with several reservoirs at different levels. A particle-based numerical simulation utilizing the Lattice Boltzmann Method (LBM) is used to simulate the flow behavior around the OWEC. Additionally, an experimental model is also built and tested in a small wave flume in order to validate the numerical results. A comparison in energy captured performance between single-level and multi-level devices has been proposed using the hydraulic efficiency. The enhancement of power capture performance is accomplished by increasing an overtopping flow rate captured by the extra reservoirs. However, a noticeably large opening of the extra reservoirs can result in a reduction in the power efficiency. The overtopping flow behavior into the reservoirs is also presented and discussed. Moreover, the results of hydrodynamic performance are compared with a similar study, of which a similar tendency is achieved. Nevertheless, the LBM simulations consume less computational time in both pre-processing and calculating phases.

On Mathematical Analysis of Active Drug Transport Coupled With Flow-Induced Diffusion in Blood Vessels.

Blood vessels are flow-induced diffusive molecular channels equipped with transport mechanisms across their walls for conveying substances between the organs in the body. Mathematical modeling of the blood vessel as a molecular transport channel can be used for the characterization of the underlying processes and higher-level functions in the circulatory system. Besides, the mathematical model can be utilized for designing and realizing nano-scale molecular communication systems for healthcare applications including drug delivery systems. In this paper, a continuous-time Markov chain framework is proposed to simply model active transport mechanisms e.g. transcytosis, across the single-layered endothelial cells building the inner vessel wall. Correspondingly, a general homogeneous boundary condition over the vessel wall is introduced. Coupled with the derived boundary condition, the flow-induced diffusion problem in an ideal vessel structure with a cylindrical shape is accurately formulated which takes into account variation in all three dimensions. The corresponding concentration Green's function is analytically derived in terms of a convergent infinite series. Particle-based simulation results confirm the proposed analysis. Also, the effects of system parameters on the concentration Green's function are examined.

Dynamical Properties of Concentrated Suspensions of Block Copolymer Stars in Shear Flow.

Block copolymer stars (BCSs) have been demonstrated to constitute versatile, self-assembling building blocks with tunable softness, functionalization, and shape. We investigate the dynamical properties of suspensions of short-arm BCSs under linear shear flow by means of extensive particle-based multiscale simulations. We determine the properties of the system for representative values of monomer packing fraction ranging from semidilute to concentrate regimes. We systematically analyze the formed network structures as a function of both shear rate and packing fraction, the reorganization of solvophobic patches, and the corresponding radial correlation functions. Connecting our findings with rheology, we calculate the viscosity as a function of shear rate and discuss the implications of the found shear thinning behavior.

Modeling the Microstructure and Stress in Dense Suspensions under Inhomogeneous Flow.

Under inhomogeneous flow, dense suspensions exhibit behavior that violates the conventional homogeneous rheology. Specifically, one finds flowing regions with a macroscopic friction coefficient below the yielding criterion, and volume fraction above the jamming criterion. We demonstrate the underlying physics by incorporating shear rate fluctuations into a recently proposed tensor model for the microstructure and stress, and applying the model to an inhomogeneous flow problem. The model predictions agree qualitatively with particle-based simulations.

3-D Diffusive Molecular Communication With Two Fully-Absorbing Receivers: Hitting Probability and Performance Analysis

Exact analytical channel models for molecular communication via diffusion (MCvD) systems involving multiple fully absorbing receivers (FARs) in a three-dimensional (3-D) medium are hard to obtain due to the mathematical intractability of corresponding diffusion equations. Therefore, this work considers an MCvD system with two spherical FARs in a 3-D diffusion-limited medium and develop several insights using an approximate analytical expression for the hitting probability of information molecule (IM). Further, based on the hitting probability, a novel approximate closed-form analytical expression for the area under the receiver operating characteristic curve (AUC) is derived to analyze the detection performance at each FAR in the presence of other FAR. Finally, simulation results are presented to validate the analytical results using the particle-based and Monte-Carlo simulations and to yield important insights into the MCvD system performance with two FARs.

Patterns in magnetic granular media at the crossover from two to three dimensions.

We perform three-dimensional particle-based simulations of confined, vibrated, and magnetizable beads to study the effect of cell geometry on pattern selection. For quasi-two-dimensional systems, we reproduce previously observed macroscopic patterns such as hexagonal crystals and labyrinthine structures. For systems at the crossover from two to three dimensions, labyrinthine branches shorten and are replaced by triplets of beads forming upright triangles which self-organize into a herringbone pattern. This transition is associated with increases in both translational and orientational orders.

Modeling of Cell-Mediated Self-Assembled Colloidal Scaffolds.

A critical step in tissue engineering is the design and synthesis of 3D biocompatible matrices (scaffolds) to support and guide the proliferation of cells and tissue growth. The most existing techniques rely on the processing of scaffolds under controlled conditions and then implanting them in vivo, with questions related to biocompatibility and implantation that are still challenging. As an alternative, it was proposed to assemble the scaffolds in loco through the self-organization of colloidal particles mediated by cells. To overcome the difficulty to test experimentally all the relevant parameters, we propose the use of large-scale numerical simulation as a tool to reach useful predictive information and to interpret experimental results. Thus, in this study, we combine experiments, particle-based simulations, and mean-field calculations to show that, in general, the size of the self-assembled scaffold scales with the cell-to-particle ratio. However, we have found an optimal value of this ratio, for which the size of the scaffold is maximal when the cell-cell adhesion is suppressed. These results suggest that the size and structure of the self-assembled scaffolds may be designed by tuning the adhesion between cells in the colloidal suspension.

Numerical simulation of heat transfer behavior in EAGLE ID1 in-pile test using finite volume particle method

The EAGLE ID1 test was performed by the Japan Atomic Energy Agency to demonstrate the effectiveness of fuel discharge from a fuel subassembly with an inner duct structure. The experimental results suggested that the early duct wall failure observed in the test was initiated by high heat flux from the molten pool and the post-test analyses showed that the high heat flux may be enhanced effectively by molten steel. In this study, the mechanisms of heat transfer were analyzed using a fully Lagrangian approach based on the finite volume particle method. A series of physical behaviors in the pool were simulated to investigate the effect of the behaviors on molten pool-to-duct wall heat transfer in ID1 test. The present 2D particle-based simulation demonstrated that a large thermal load on the duct wall can be caused by direct contact of the liquid fuel releasing nuclear heat and high-temperature liquid steel.

Particle-based simulation of cold spray: Influence of oxide layer on impact process

High-speed cold spraying of micro-particles, at temperatures well below their melting point, impinging on desired substrates is a useful emerging additive process. One principal element believed to dramatically influence the bonding strength and final coating quality of many metallic cold-spray deposits is the native oxide layer on the surface of the impinging particles and substrate. Here, smoothed-particle hydrodynamics (SPH), a mesh-free, particle-based method, is used to simulate cold-spraying, including the action of oxide layers in the deposition and bonding processes. The effect of increasing particle impact velocity on oxide breakup and subsequent bonding is studied. Particle/substrate bonding is quantified via metallic interaction at the interface and found to continuously improve with increasing velocity well beyond the critical velocity. The coefficient of restitution is also calculated for different oxide layer thicknesses on the particle and the substrate. The effects of particle diameter and oxygen content on the impact process are reported and compared to results from the literature. The particle-based simulation approach is found to reproduce experimentally characterized trends in oxide content effect and provide insight into the mechanisms of oxide breakup during bonding.