Particle Configurations Research Articles

A bio-lattice deep learning framework for modeling discrete biological materials.

Biological tissues dynamically adapt their mechanical properties at the microscale in response to stimuli, often governed by discrete interacting mechanisms that dictate the material's behavior at the macroscopic scale. An approach to model the discrete nature of these elemental units is the Lattice Spring Modeling (LSM). However, the interactions in biological matter can present a high degree of complexity and heterogeneity at the macroscale, posing a computational challenge in multiscale modeling. In this work, we propose a novel machine learning-based multiscale framework that integrates deep neural networks (DNNs), the finite element method (FEM), and a LSM-inspired microstructure description to investigate the behavior of discrete, spatially heterogeneous materials. We develop a versatile, assumption-free lattice framework for interacting discrete units, and derive a consistent multiscale connection with our FEM implementation. A single DNN is trained to learn the constitutive equations of various particle configurations and boundary conditions, enabling rapid response predictions of heterogeneous biological tissues. We demonstrate the effectiveness of our approach with extensive testing, starting with benchmark cases and progressively increasing the complexity of the microstructures. We explored materials ranging from soft to hard inclusions, then combined them to form a macroscopically homogeneous material, a gradient-varying polycrystalline solid, and fully randomized configurations. Our results show that the model accurately captures the material response across these spatially varying structures.

An Efficient SPH Framework for Modeling Binary Granular Mixtures and Implications for Granular Flows

ABSTRACTA two‐way coupling numerical framework based on smoothed particle hydrodynamics (SPH) is developed in this study to model binary granular mixtures consisting of coarse and fine grains. The framework employs updated Lagrangian SPH to simulate fine grains, with particle configurations updated at each time step, and total Lagrangian SPH to efficiently model coarse grains without updated particle configurations. A Riemann solver is utilized to introduce numerical dissipation in fine grains and facilitate their coupling with coarse grains. To enhance computational efficiency, a multiple time‐stepping scheme is initially applied to manage the time integration coupling between coarse and fine grains. Several numerical experiments, including granular column collapse, low‐speed impact craters, and granular flow impacting blocks, are conducted to validate the stability and accuracy of the proposed algorithm. Subsequently, two more complex scenarios involving a soil–rock mixture slope considering irregular coarse particle shapes, and bouldery debris flows on natural terrain, are simulated to showcase the potential engineering applications. Finally, a detailed analysis is performed to evaluate the computational efficiency advantages of the present approach. The findings of this study are consistent with previous experimental and numerical results, and the implementation of a multiple time‐stepping scheme can improve computational efficiency by up to 600%, thereby providing significant advantages for large‐scale engineering simulations.

ECW assisted plasma startup with low toroidal electric field and full metal wall in EAST superconducting tokamak

Abstract Recent experimental results from the EAST superconducting tokamak with a full metal wall (without Li-coating) provide strong support for the ITER startup scheme with electron cyclotron wave (ECW) assistance. Detailed investigations were performed on the impact of different magnetic field geometries, including quadrupolar field configuration (QFC), wide null field configuration (NFC), and trapped particle configuration (TPC). All three configurations enabled the breakdown of neutral gas with ECW pre-ionization. However, in most discharges with QFC, the absence of closed flux surfaces after the breakdown was associated with unsuccessful plasma startups. Conversely, both NFC and TPC achieved successful plasma startups under normal conditions, with TPC demonstrating higher robustness against high impurity levels compared to NFC. Experiments found that the minimum ECW power required for a successful TPC startup is approximately 0.48 MW. Plasma startup failed if the prefill gas pressure exceeded optimal levels for a given ECW power (≥0.48 MW), with the critical gas pressure linearly dependent on the ECW power. Additionally, higher ECW power was required with increased impurity levels. With ECW assistance, the toroidal electric field at R=1.85 m in EAST routine operations (<0.15 V/m) is significantly lower than ITER's maximum value (0.3 V/m), suggesting the possibility of reducing flux consumption and lowering demands on coils and power supplies for ITER.

Momentum shift and on-shell constructible massive amplitudes

We construct tree-level amplitude for massive particles using on-shell recursion relations based on two classes of momentum shifts: an all-line transverse shift that deforms momentum by its transverse polarization vector, and a massive Britto-Cachazo-Feng-Witten-type shift. We illustrate that these shifts allow us to correctly calculate four-point and five-point amplitudes in massive QED, without an ambiguity associated with the contact terms that may arise from a simple “gluing” of lower-point on-shell amplitudes. We discuss various aspects and applicability of the two shifts, including the large-z behavior and complexity scaling. We show that there exists a “good” all-line transverse shift for all possible little group configurations of the external particles, which can be extended to a broader class of theories with massive particles such as massive QCD and theories with massive spin-1 particles. The massive Britto-Cachazo-Feng-Witten-type shift enjoys more simplicity, but a “good” shift does not exist for all the spin states due to the specific choice of spin axis. Published by the American Physical Society 2024

Cell theories for the chiral crystal phase of hard equilateral triangles.

We derive several versions of the cell theory for a crystal phase of hard equilateral triangles. To that purpose we analytically calculated the free area of a frozen oriented or freely rotating particle inside the cavity formed by its neighbors in a chiral configuration of their orientations. From the most successful versions of the theory we predict an equationof state which, despite being derived from a crystal configuration of particles, describes very reasonably the equationof state of the 6-atic liquid-crystal phase at packing fractions not very close from the isotropic-6-atic bifurcation. Also, the same equationof state performs well when compared to that from MC simulations for the stable crystal phase. The agreement can even be improved by selecting adequate values for the angle of chirality. Despite the success of two versions of the theory, we show that the free energy is an increasing function of the angle of chirality, implying that the most stable phase is the achiral phase. Furthermore, we show that possible clustering effects, such as the formation of perfect chiral hexagonal clusters, which in turn crystallize into an hexagonal lattice, cannot explain the presence of the chirality observed in simulations.

Spatial‐Dependent Coupling of Electrochemistry, Mass Transport, and Stress in Silicon‐Graphite Composite Electrodes for Lithium‐Ion Batteries

AbstractThe commercialization of high‐capacity silicon materials in lithium‐ion batteries is hindered by significant volume changes. Composite anodes made from silicon and graphite, which increase battery capacity and maintain electrode structural stability, are receiving considerable attention. However, the spatial configuration of the active particles in the electrode is few investigated due to the complexity of experiments at the microscopic scale. Herein, this work focuses on electrochemical, mass transport, and stress coupling mechanisms by considering different spatial configurations of silicon and graphite. In situ electrochemical and stress measurements are first conducted to demonstrate the impact of the active material arrangement. Then, a 2D electrochemical‐mechanical model is developed considering the heterogeneity of electrochemical processes at the particle‐electrolyte interface. The results show that placing silicon in the upper active layer significantly reduces the ion transport resistance, while the graphite layer near the current collector provides a good conductive network for electron transport in the silicon layer, enhancing the performance of the composite electrode structure. By combining electrochemical and mechanical field models with experimental verification, this study deepens the understanding of composite electrode structure design, offering practical guidance for optimizing the spatial configuration of electrode materials to significantly improve battery performance.

The Lubin–Tate theory of configuration spaces: I

AbstractWe construct a spectral sequence converging to the Lubin–Tate theory, that is, Morava ‐theory, of unordered configuration spaces and identify its ‐page as the homology of a Chevalley–Eilenberg‐like complex for Hecke Lie algebras. Based on this, we compute the ‐theory of the weight summands of iterated loop spaces of spheres (parameterizing the weight operations on ‐algebras), as well as the ‐theory of the configuration spaces of points on a punctured surface. We read off the corresponding Morava ‐theory groups, which appear in a conjecture by Ravenel. Finally, we compute the ‐homology of the space of unordered configurations of particles on a punctured surface.

Mesoscale simulation of granular materials under weak shock compaction–pore size distribution effects

This research established a systematic method to generate various pore-size distributions (PSDs) and studied the effect of PSDs on the shock compaction response of granular materials using two-dimensional mesoscale simulations under identical porosity. Simulations utilized various PSDs for three particle shapes (circle, ellipse, and square). Contacting particle configurations using three PSDs, characterized by spatially uniform distributed pores to heterogeneous distributed pores, and non-contacting particle configurations under a single case of PSD were tested. The PSD of generated particle sets was characterized using coordination number, mean diameter, and bimodality coefficient as statistical metrics. Mesoscale simulations showed that regardless of the conditions of pore distributions, shock compaction of granular materials consistently demonstrates a precursor, shock compaction front, and end. However, the shock compaction velocity of contacting particles was dependent on the PSDs despite the constant initial porosity. The compaction velocity was faster in particle configurations with relatively uniform pore distributions than in heterogeneous pore distributions, which our study demonstrated can be attributed to particle rearrangement during compaction. Circular-shaped particles had high sensitivity in shock compaction response to the various PSDs. Furthermore, a contacting particle configuration tended to propagate the shock compaction wave relatively faster than particles that were in a non-contact configuration. This study established the relative importance of considering PSD as a metric over the coordination number in studies of the shock compaction response of granular materials. Further, insights are provided on the evolving shock substructure to characterize the shock compaction response of granular materials.

Power dissipation and entropy production rate of high-dimensional optical matter systems.

Entropy production is an essential aspect of creating and maintaining nonequilibrium systems. Despite their ubiquity, calculation of entropy production rates is challenging for high-dimensional systems, so it has only been reported for simple (i.e., l-particle) systems. Moreover, there is a dearth of nontrivial experimental systems where precise measurements of entropy production rate and characterization of the nonequilibrium steady state (NESS) are simultaneously possible. We report an approach to calculate the entropy production rate of overdamped, nonconservative, N-body systems and demonstrate this on a six-particle triangle optical matter (OM) system as a nontrivial example. OM systems consist of (nano-)particles organized into ordered arrays that are bound by electrodynamic interactions associated with the scattering and interference of light, and the associated induced-polarizations in and among the particles in coherent optical beams. The flux of laser light in OM systems in a solution environment necessitates that they dissipate energy, produce entropy, and relax to a NESS. The NESS may have several ordered particle configurations (i.e., isomers) that can interchange by barrier crossing processes. Understanding the power dissipation and entropy production rate of a NESS in an OM system along different (collective) modes of motion can advance understanding of the relative stability of the NESSs as well as inform design and control of OM structures. Therefore, we compute the components of the entropy production rate and power dissipation along the collective coordinates of the 6 Ag nanoparticle triangle OM system from OM NESS trajectory data and verify the Seifert relation [U. Seifert, Rep. Prog. Phys. 75, 126001 (2012)10.1088/0034-4885/75/12/126001] for these complex systems with a nuanced interpretation.

Experimental investigation of particle impact dampers: Vibration reduction and noise levels with varied particle numbers

Structural vibrations present significant challenges in engineering systems, and particle impact dampers (PIDs) have emerged as a promising solution for vibration control. This paper presents a comprehensive experimental investigation on the damping performance of PIDs with varying numbers of particles. The experimental setup involves subjecting a single-degree-of-freedom (SDOF) structure to base motion excitations. Frequency response analysis is conducted to assess the dynamic behavior of the primary system using different PID designs. PIDs with one mass, two masses, three masses and multiple particles are designed and analyzed using the same mass ratio. Additionally, time domain displacement responses of the top of the structure and the base are measured to capture the system's response characteristics. The frequency response analysis reveals the influence of different particle configurations on the system's response amplitude at various frequencies. Interestingly, the results show that a PID with multiple particles exhibits reduced damping performance due to excessive particle-particle collisions. These collisions decrease the energy of the particles, leading to lower velocity before impact. This highlights that the impact itself is the primary source of damping in PIDs, and any other phenomena, such as particle-particle collisions, directly affect the relative velocity before impact. The outcomes demonstrate that an optimal design of PID can reduce vibration amplitude by approximately 85 % with single mass, two masses, and three masses configurations. Conversely, the best-case scenario of PID with multiple particles provides a vibration amplitude reduction of 75 % compared to the absence of a damper. Furthermore, noise data is recorded for all cases, as noise generation is a significant concern associated with PIDs. The results indicate noise magnitudes of 62.5 dB, 70.60 dB, 73.24 dB and 78.64 dB for PIDs with single mass, two masses, three masses, and multiple particles, respectively. Overall, the relationship between the number of particles and damping performance provides valuable insights for optimizing PID designs and ensuring comfortable operation. The findings have broad implications for diverse engineering systems, leading to improved structural performance, reduced noise, and enhanced safety. This study could serve as the basis for several potential applications of PID in structural vibration control with enhanced performance and reduced noise.

Real-time induced magnetic vibrational based antifouling mechanism for ultrafiltration (UF) membrane

Despite the widespread adoption of membrane technologies for efficient water treatment, membrane fouling remains a significant challenge, reducing separation efficiency, shortening lifespan, and increasing operational costs. Various studies have explored chemical membrane modifications to mitigate fouling, often resulting in adverse effects on flux and selectivity. Based on numerical modeling and experimental investigation, this work introduces real-time induced magnetic vibration as a sustainable approach for membrane antifouling without compromising permeability and selectivity. By preventing or delaying particle deposition on the membrane surface, magnetic vibration reduces fouling intensity. Experimental results demonstrated that different frequencies of magnetic vibration influenced the deposition of foulants (Humic Acid and Sodium Alginate) on the membrane surface. Notably, vibrating the membrane at 10 Hz with centrally attached iron particles led to a 22.4 % reduction in flux when treated with Humic Acid, compared to a 33.9 % reduction without vibration. Exposure to vibrations at the resonance frequency (5 Hz) for 6 h resulted in only a 10 % reduction in flux, effectively preventing the formation of a dense cake layer. Similarly, in the case of Sodium Alginate, a 10 Hz vibration for 2 h decreased the flux reduction from 21.4 % without vibration to 7.3 %, suggesting the preventive effect of vibration on aggregated SA deposition or facilitating continuous displacement for flux retention. Moreover, the study examined the influence of the configuration of iron particles attached to the membranes on the effectiveness of vibration. The study revealed that a striped configuration was more effective than a centralized configuration, owing to the distributed vibration effect across each part of the membrane. Furthermore, the fouling mechanism and rejection percentage were further investigated to enhance understanding of the fouling processes.

Probing the internal structures of pΩ and ΩΩ with their production at the Large Hadron Collider

The strange dibaryons pΩ (S25) and ΩΩ (S01) are likely bound, existing either in molecular states like the deuteron or as more exotic compact six-quark states. Here, we investigate the production of these two dibaryons in Pb+Pb collisions at sNN=2.76 TeV at the CERN Large Hadron Collider within a covariant coalescence model, which employs a blast-wave-like parametrization for the phase-space configurations of constituent particles at freeze-out. For the molecular states, the pΩ and ΩΩ are produced via p−Ω and Ω−Ω coalescence, respectively, while for the six-quark states, they are formed through uudsss and ssssss coalescence. We find that the yield ratios NpΩ/NΩ and NΩΩ/NΩ have a distinct centrality dependence between the molecular and multiquark states, thus offering a promising way for distinguishing the two states. Our results suggest that the measurements of pΩ and ΩΩ production in relativistic heavy-ion collisions can shed light on their internal structures. Published by the American Physical Society 2024

Flow field prediction in bed configurations: A parametric spatio-temporal convolutional autoencoder approach

The use of deep learning methods for modeling fluid flow has drawn a lot of attention in the past few years. Here we present a data-driven reduced-order model (ROM) for predicting flow fields in a bed configuration of hot particles. The ROM consists of a parametric spatio-temporal convolutional autoencoder. The neural network architecture comprises two main components. The first part resolves the spatial and temporal dependencies present in the input sequence, while the second part of the architecture is responsible for predicting the solution at the subsequent timestep based on the information gathered from the preceding part. We also propose the utilization of a post-processing non-trainable output layer following the decoding path to incorporate the physical knowledge, e.g. no-slip condition, into the prediction. The ROM is evaluated by comparing its predicted solution with the high-fidelity counterpart. In addition, proper orthogonal decomposition (POD) is employed to systematically analyze and compare the dominant structures present in both sets of solutions. The assessment of the ROM for a bed configuration with variable particle temperature showed accurate results at a fraction of the computational cost required by traditional numerical simulation methods.

Stress correlations in near-crystalline packings

We derive exact results for stress correlations in near-crystalline systems in two and three dimensions. We study energy minimized configurations of particles interacting through Harmonic as well as Lennard-Jones potentials, for varying degrees of microscopic disorder and quenched forces on grains. Our findings demonstrate that the macroscopic elastic properties of such near-crystalline packings remain unchanged within a certain disorder threshold, yet they can be influenced by various factors, including packing density, pressure, and the strength of inter-particle interactions. We show that the stress correlations in such systems display anisotropic behavior at large lengthscales and are significantly influenced by the pre-stress of the system. The anisotropic nature of these correlations remains unaffected as we increase the strength of the disorder. Additionally, we derive the large lengthscale behavior for the change in the local stress components that shows a 1/r^d1/rd radial decay for the case of particle size disorder and a 1/r^{d-1}1/rd−1 behavior for quenched forces introduced into a crystalline network. Finally, we verify our theoretical results numerically using energy-minimised static particle configurations.

Improving laser powder bed fusion processability of pure Cu through powder functionalization with Ag

The Laser Powder Bed Fusion (LPBF) manufacturing of dense Cu parts with near infrared conventional systems is still challenging due to the high reflectivity and thermal conductivity of the powder. In this paper, we investigate a novel approach to improve LPBF processability of Cu through the modification of particle surface properties. Pure Cu powders were coated with a thin layer of high-conductivity Ag by electrodeposition. The coated powder was also heat-treated at 500 °C and 600 °C to promote diffusion at the coating interface, producing different powder particle configurations. LPBF equipped with 200 W laser was used to produce bulk samples using pure Cu and Cu/Ag powder, which were comprehensively characterized by electron microscopy and X-ray diffraction. The Ag coating improved the material processability and density, forming a eutectic phase mixture able to heal pores and defects at the end of the solidification process.

Universal mechanism of shear thinning in supercooled liquids

Soft glassy materials experience a significant reduction in viscosity η when subjected to shear flow, known as shear thinning. This phenomenon is characterized by a power-law scaling of η with the shear rate γ°\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\dot{\\gamma }$$\\end{document}, η∝γ°−ν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta \\propto {\\dot{\\gamma }}^{-\ u }$$\\end{document}, where the exponent ν is typically around 0.7 to 0.8 across different materials. Two decades ago, the mode-coupling theory (MCT) suggested that shear thinning occurs due to the advection. However, it predicts too large ν = 1 ( > 0.7 to 0.8) and overestimates the onset shear rate by orders of magnitude. Recently, it was claimed that a minute distortion of the particle configuration is responsible for shear thinning. Here we extend the MCT to include the distortion, and find that both advection and distortion contribute to shear thinning, but the latter is dominant. Our formulation works quantitatively for several different glass formers. We explain why shear thinning is universal for many glassy materials.

Controlling the columnar-to-equiaxed transition and crack propagation behavior of laser welded Al–Li alloy reinforced with TiC nanoparticles

The predominant method for improving the mechanical characteristics of aluminum-lithium (Al–Li) alloys during laser welding is by regulating the transition from columnar crystals to equiaxed crystals. The transition is facilitated through the incorporation of TiC particles, which play a crucial role in augmenting the strength and toughness of the aluminum alloy. The inclusion of TiC particles offers a new opportunity to enhance the utilization of Al–Li alloys in laser welding procedures. The main aim of this study is to control the transition from columnar to equiaxed crystal structures by creating a TiC nanoparticle-reinforced aluminum alloy filler wire. This is intended to improve the properties of laser-welded joints. The study investigates the impact of different TiC particle contents on the microstructural characteristics. The presence of TiC particles is noted to enhance the transformation of columnar crystals into equiaxed crystals in the welded joints, resulting in a refined microstructure. The augmentation of particle content results in a notable reduction in the average grain size, decreasing from 78.25 μm to 37.15 μm. This alteration shifts the directional growth preference from specific crystallographic axes to a stochastic trajectory. Significantly, when the particle content reaches 0.6 wt%, remarkable enhancements in both tensile strength and elongation are evident, resulting in an ultimate tensile strength of 318 MPa and an elongation of 4.8 %. The dispersed configuration of TiC particles plays a pivotal role in hindering the movement of dislocations, consequently increasing the energy necessary for this mechanism. Moreover, the existence of these particles at grain boundaries impedes the propagation of cracks by altering the direction of the crack path, absorbing additional energy, and consequently improving toughness. An evident pattern emerges when examining higher particle concentrations, indicating that a decrease in ultimate tensile strength is concomitant with an increase in elongation.

Lambda motion and cluster states of [formula omitted] predicted via neural networks guided microscopic calculation

We investigate the Λ particle motion and cluster states of BeΛ9−11 using a novel multiple cooling approach guided by the Control Neural Networks method (Ctrl.NN). The numerical results are well reproduced, and the Ctrl.NN method shows rapid convergence concerning the set of the basis states in comparison to traditional generator coordinate method (GCM) calculations. Taking advantage of this merit, we calculate ground state energies and rotational bands of BeΛ9−11 which agree well with experimental observations. By analyzing the density distributions of the main basis, a slight short-range repulsive correlation between Λ particle and α clusters in directions perpendicular to the axis of the Be8 core is revealed. The Λ particle presents a significant broad distribution as a consequence of this correlation, which we call an “analog π-orbit” structure. Furthermore, we prove that this correlation originates from the competition between kinetic energy and Λ-N interaction, as indicated by the energy curves for different configurations of BeΛ9. In the ground states of BeΛ10 and BeΛ11, the Λ particle is confined in a cage-like structure of core nuclei formed by two α clusters and valence neutrons. These structures lead to the development of more compact Λ particle configurations, and the shrinkage of α-α relative distance is also well reproduced.

Effective T-matrix of a cylinder filled with a random two-dimensional particulate

When a wave, such as sound or light, scatters within a densely packed particulate, it can be rescattered many times between the particles, which is called multiple scattering. Multiple scattering can be unavoidable when trying to use sound waves to measure a dense particulate, such as a composite with reinforcing fibres. Here, we solve from first principles multiple scattering of scalar waves, including acoustic, for any frequency from a set of two-dimensional particles confined in a circular area. This case has not been solved yet, and its solution is important to perform numerical validation, as particles within a cylinder require only a finite number of particles to perform direct numerical simulations. The method we use involves ensemble averaging over particle configurations, which leads us to deduce an effective T-matrix for the whole cylinder, which can be used to easily describe the scattering from any incident wave. In the specific case when the particles are monopole scatterers, the expression of this effective T-matrix simplifies and reduces to the T-matrix of a homogeneous cylinder with an effective wavenumber k ⋆ . To validate our theoretical predictions, we develop an efficient Monte Carlo method and conclude that our theoretical predictions are highly accurate for a broad range of frequencies.

Enhancing particle string detection in electrorheological plasmas using asymmetrical kernel convolutional networks

Under different plasma conditions and electric fields in a complex plasma the plasma particles organize themselves in a string-like or chain-like manner. A phase transition from string-like to an isotropic particle distribution is observed at different electrical conditions. The streaming of charged ions around plasma particles with the surrounding electric field gives the plasma its electrorheological properties. The visibility of individual particles in a complex plasma opens up the opportunity to examine properties and phase transitions of such electrorheological fluids in detail. Because of the limited one-dimensional symmetry, determining the configuration of a particle and recognizing strings in particle distributions is not always straightforward. Several approaches have already been used to analyse particle clouds while either considering each particle locally or considering the particle cloud as a whole without providing information about single particle configurations. This paper presents a new machine learning approach that takes advantage of particle distributions over the entire particle cloud and detects all string-like particles at once, using a convolutional neural network in form of an encoder-decoder network with asymmetric kernel convolutions. This not only enhances the result quality but also accelerates the evaluation process, possibly enabling real-time analyses on electrorheological phase transitions, while achieving an accuracy of over 95% on manually labelled data.