Random Distribution Of Particles Research Articles

Investigation of the Thermophysical Simulation and Material Removal Mechanism of the High-Volume-Fraction SiCp/Al Composite in Wire Electrical Discharge Machining.

SiC particle reinforced aluminum matrix composites (SiCp/Al) are widely used in aviation, weaponry, and automobiles because of their excellent service performance. Wire electrical discharge machining (WEDM) regardless of workpiece hardness has become an alternative method for processing SiCp/Al composites. In this paper, the temperature distribution and the discharge crater size of the SiCp/Al composite are simulated by a thermophysical model during a single-pulse discharge process (SPDP) based on the random distribution of SiC particles. The material removal mechanism of the SiCp/Al composite during the multi-pulse discharge process (MPDP) is revealed, and the surface roughness (Ra) of the SiCp/Al composite is predicted during the MPDP. The thermophysical model simulation results during the MPDP and experimental characterization data indicate that the removal mechanism of SiCp/Al composite material consists of the melting and vaporization of the aluminum matrix, as well as the heat decomposition and shedding of silicon carbide particles. Pulse-on time (Ton), pulse-off time (Toff), and servo voltage (SV) have a great influence on surface roughness. The Ra increases with an increase in Ton and SV, but decreases slightly with an increase in Toff. Moreover, compared with experimental data, the relative error of Ra calculated from the thermophysical model is 0.47-7.54%. This means that the developed thermophysical model has a good application and promotion value for the WEDM of metal matrix composite material.

Finite element simulation of the effect of particle shape and thermal residual stress on the tensile properties of NbCp/Fe composites

To optimize the microstructure and properties of NbCp/Fe composites, this study considered factors such as load transfer, plastic strain, matrix damage, and interface debonding. A two-dimensional finite element model was constructed using ABAQUS software. Random adsorption algorithm was used to realize the random distribution of particles within the representative volume element, and interface damage was described using a cohesive zone model. The effects of particle shape and thermal residual stress on the tensile properties of NbCp/Fe composites were investigated. The results show that circular particles uniformly distribute the stress during the tensile process, effectively enhancing the tensile properties of the composites. Conversely, triangular particles, with obvious sharp corners, reduce their tensile properties, particularly in the presence of thermal residual stresses, which further aggravates the stress concentration between the particles and the matrix. This leads to a decrease in the strength and toughness of the material.

Statistical evaluation of electric field distributions in 3D composites with a random spatial distribution of dielectric inclusions

Electromagnetic applications of composites often impose constraints on the internal electric fields, such as an upper limit on the field strength to prevent local heating or dielectric breakthrough. However, owing to heterogeneity, the local fields in a composite differ from those in a homogeneous material. Moreover, they are accessible neither by experiment nor by effective medium theories, at least for arbitrary microstructures. In this work, we use numerical simulations to evaluate the electric field distribution and the effective permittivity for 3D systems of monodisperse impenetrable spheres dispersed in a continuous matrix phase. We restrict ourselves to loss-free dielectric materials and to a random spatial distribution of particles. Samples are placed in a parallel plate waveguide and exposed to a transverse electromagnetic wave. The local field amplitudes are calculated via the finite element method and are normalized to those of a homogeneous sample exhibiting the same effective permittivity and geometry. We analyze the distribution of the local electric field strength in both constituents, namely, particles and matrix. Thus, we evaluate mean values and standard deviations as well as the field strengths characterizing the highest and lowest percentiles. Increasing particle concentration or permittivity enhances heterogeneity, and so the local electric field strength in some domains can become much higher than its average value. The methods we apply here can also be used in further investigations of more complex systems, including lossy materials and agglomerating particles.

Fast and Accurate Recognition of Perovskite Fluorescent Anti-counterfeiting Labels Based on Lightweight Convolutional Neural Networks.

Anti-counterfeiting technology has always been a key issue in the field of information security. Physical Unclonable Function (PUF) labels, which are random patterns produced by a stochastic process, emerge as an effective anti-counterfeiting strategy due to the inherent randomness of their physical patterns. In this study, we developed a high-throughput droplet array generation technique based on surface tension confinement to prepare perovskite crystal films with controllable shapes and sizes. We utilized the random distribution of perovskite nanocrystal particles to construct the PUF textures of the labels. Compared to other anti-counterfeiting labels, our labels not only possess fluorescent properties but also feature microscale dimensions (less than 5.3 × 10-2mm2), low cost (less than 3 × 10-4 USD), and high encoding capacity (1.7 × 101956), providing support for multilevel anti-counterfeiting protection. Additionally, we introduce an innovative PUF recognition method based on a Partial Convolutional Network (PaCoNet), effectively addressing the limitations of previous methods, in terms of recognition accuracy and speed. Experimental validation on a data set of perovskite nanocrystal films with up to 60 different macroscopic shapes and unique microscopic textures demonstrates that our method achieves a recognition accuracy of up to 99.65% and significantly reduces the recognition time per image to just 0.177 s, highlighting the potential application of these labels in the field of anti-counterfeiting.

Micro-cracks evolution process and borehole stability analysis in deep pebbly sandstone based on THM coupling method

The heterogeneity of pebbly sandstone is strong due to the random distribution of gravel particles, resulting in unique mechanical behaviors and failure patterns under high temperature and high pressure. It poses significant challenges to exploring and developing oil and gas resources in deep pebbly sandstone reservoirs. Thus, to investigate the impact of high pressure, high temperature, and gravel distribution on the strength characteristics and failure patterns of pebbly sandstone, based on typical samples from the Tarim Basin, a numerical model that accurately represents the distribution of gravel particles in pebbly sandstone was built. Lastly, a Thermo-Hydro-Mechanical coupling model for pebbly sandstone boreholes was established to quantify the influence of the difference coefficient of in-situ stress and gravel volume fraction. The results show that during the rupturing process of pebbly sandstone, the micro-cracks initiation occurs and expands around the gravel, forming a shear-type fracture. At the same time, several secondary cracks are formed inside the rock. With increasing confining pressure, the number of secondary cracks decreases. The fracture morphology is complex in concentrated gravel distribution, whereas a single fracture dominates the failure pattern in uniform distribution. The micro-cracks are more likely to occur in the gravel areas around the borehole during drilling, resulting in the gravel accumulation areas prioritizing collapse rather than in the direction of minimum horizontal in-situ stress, significantly reducing the safe density window. The safety density window decreases with the increase of the gravel volume but increases when the gravel volume fraction is higher than 20%. The research results provide valuable guidance for efficient and safe drilling in pebbly sandstone formations.

Multi-mode multiple wave scattering in suspensions of solid particles in viscous liquids: part 1 asymptotic results

The propagation of coherent longitudinal waves in a viscous liquid containing a random distribution of spherical elastic particles is analysed using a model based on multiple scattering theory. This model, initially developed to describe the propagation of coupled longitudinal and transverse elastic waves in isotropic solid materials, takes into account wave conversions at the particle surface as well as positional correlations between particles via the pair correlation function. By modelling a Newtonian viscous liquid through complex frequency-dependent elastic moduli (i.e. using an imaginary shear modulus), the model is adapted to the case of viscous fluids and we show that conversions of longitudinal waves into transverse waves are important to take into account. We present asymptotic results from the model for case of long wavelength for the longitudinal waves, without assumptions on the shear wavelength. For high concentrations, these wave conversions are reinforced by the contribution of position correlations between the particles. In a subsequent paper, we present numerical validation of the asymptotic approximations and compare them with experimental results for solid particles in water.

A comprehensive study on predicting the elastic modulus and Poisson ratio of hardened cement pastes via micro-scale cement hydration simulations

A comprehensive study was conducted to predict the elastic modulus and Poisson ratio of hardened cement pastes (HCPs), including numerical work, comparison of two modelling approaches, and experimental validation. Modelling approach 1 (M1) applied a continuum micromechanics model (CMM) whereas modelling approach 2 (M2) used finite element analysis (FEA). This study presents a reliable method for predicting the mechanical properties of HCPs by firstly employing the microstructures of HCPs, obtained by CEMHYD3D analysis with random distribution of cement particles as input parameters for both M1 and M2 approaches. The elastic modulus and Poisson ratio predicted by M1 and M2 approaches were verified through the experimental data obtained from compressive tests combined with digital image correlation analysis. The elastic moduli of HCPs were significantly affected by the input C-S-H modulus, with 90% of reported C-S-H elastic moduli falling within the range of 20.25 GPa and 30.4 GPa. The predicted elastic moduli of HCPs were well-matched with experimental data when assigning the elastic modulus, bulk modulus, and shear modulus, of C-S-H, as 20.25, 12.98, and 8.17 GPa, respectively. A sensitivity analysis demonstrated a linear increase in the elastic modulus of HCPs with increase of C-S-H modulus, with correlation coefficients R² of 0.9985 and 0.9996 for HCPs with water-to-cement ratios (w/c) of 0.3 and 0.5, respectively. Additionally, the predicted results showed greater consistency at later age (after 7 d) compared to earlier stages. The integral absolute error values of the elastic moduli obtained via M1 and M2 for HCPs with w/c of 0.3 were 6.32% and 6.23%, respectively while those for HCPs with w/c of 0.5 were 4.8% and 5.67%, respectively. M1 approach is more recommended since it is straightforward with time and cost efficiency compared to M2 approach in addition to the suitable prediction accuracy.

Uncertainty Quantification of Engineering Parameters for a Nuclear Reactor Loaded with Dispersed Fuel Particles

Owing to their high intrinsic safety, dispersed fuel particles are an important fuel pattern for fourth-generation nuclear reactors. Due to the unique cladding layers and the random dispersion characteristics, dispersed fuel particles significantly differ from pressurized water reactors regarding operation-induced uncertainty. This study quantitatively analyzed overall uncertainty while considering a random distribution of dispersed fuel particles, material thickness, and fuel enrichment. The results demonstrated that, for all packing fractions, the uncertainty induced by the random dispersion of dispersed fuel particles was below 0.03%. For every packing fraction, the differences between the results obtained by the regular and the random distribution models increased, and then decreased, until reaching its maximum (1.297%) at 15%. Keff decreased as the radius of the UO2 kernel increased; Keff increased as the thickness of the cladding layer increased; the uncertainty of Keff was 1.003% when a random distribution of particles, material thickness, and fuel enrichment were taken into consideration; the uncertainty of the power distribution of reactor core assemblies was maximized (1.495%) at the edge of the reactor core. Quantitative analysis of uncertainty provides references for the optimization of design and safety margin analysis for reactors.

Irradiance Distribution and System Analysis of Optical Wave Propagation Through Turbulence in the Presence of a Random Distribution of Particles

Irradiance Distribution and System Analysis of Optical Wave Propagation Through Turbulence in the Presence of a Random Distribution of Particles

Influence of distribution and size of graphite particle on the machinability of nodular cast iron

Nodular cast iron (NCI), also known as particulate metal matrix composites (PMMCs), is extensively utilized in the manufacturing of pistons for high-power engines. However, poor machinability is a significant problem due to the inherent microstructural inhomogeneity of the NCI. Therefore, this study aims to investigate influence of graphite particle distribution and size on the machinability of NCI. Microstructure of NCI is experimentally examined and quantitatively analyzed, and NCI is treated as a three-phase composite (i.e., matrix-interface-graphite particles). Graphite particles are considered as a plastic material, and debonding of interfaces is taken into account. Based on average particle size and volume fraction of graphite nodules, four microstructural models with different particle sizes and distributions are discussed, and turning experiments are conducted to validate simulation results. The findings reveal that graphite particles of different sizes and distributions affect stress–strain distribution in the cutting region of the material. As the distance between the graphite particles and the cutting tool increases, their influence on the stress–strain distribution becomes less significant. Furthermore, random distribution of graphite particles is more likely to result in complex crack initiation and expansion, leading to more pronounced serrated chips. The distribution of graphite particles has a more significant impact on the stability of cutting forces compared to the particle size. Both experiments and simulation results show that cavities and burrs are appeared along the cutting path because of graphite nodules, which reduces the surface quality and affects the subsequent use of the parts. On the one hand, the present work provides guidance for the finite element modelling of NCI, which is helpful to improve the precision of cutting simulation. On the other hand, by integrating advanced manufacturing technology to regulate the microstructure and cutting parameter of NCI, the present study can provide data basis and numerical analysis support for improving the machinability of NCI.

Random distribution and adsorption characteristics of metal particles and visualization algorithm for DC GIS/GIL

Random distribution and adsorption characteristics of metal particles and visualization algorithm for DC GIS/GIL

Collisions among elongated settling particles: The twofold role of turbulence

We study the collision rates of settling spheres and elongated spheroids in homogeneous, isotropic turbulence by means of direct numerical simulations aiming to understand microscale-particle encounters in oceans and lakes. We explore a range of aspect ratios and sizes relevant to the dynamics of plankton and microplastics in water environments. The results presented here confirm that collision rates between elongated particles in a quiescent fluid are more frequent than those among spherical particles in turbulence due to oblique settling. We also demonstrate that turbulence generally enhances collisions among elongated particles as compared to those expected for a random distribution of the same particles settling in a quiescent fluid, although we also find a decrease in collision rates in turbulence for particles of the highest density and moderate aspect ratios (A=5). The increase in the collision rate due to turbulence is found to quickly decrease with aspect ratio, reach a minimum for aspect ratios approximately equal to 5, and then slowly increase again, with an increase up to 50% for the largest aspect ratios investigated. This non-monotonic trend is explained as the result of two competing effects: the increase in the surface area with aspect ratio (beneficial to increase encounter rates) and the alignment of nearby prolate particles in turbulence (reducing the probability of collision). Turbulence mixing is, therefore, partially balanced by rod alignment at high particle aspect ratios.

Study on effect of charged sand/dust atmosphere on the performance of microwave quantum illumination radar

This paper aims to study the effects of charged sand/dust atmosphere on the performances of microwave quantum illumination (QI) radar. Based on Mie particle scattering theory, using a Monte Carlo method for simulating the physical process which photon is scattered multiple times by discrete random distributed particles, the specific attenuation (dB/km) of microwave propagating in sand/dust atmosphere are analyzed under the conditions of varying atmospheric visibility and sand/dust particles with different charged quantities. It is indicated that the specific attenuation obtained by multiple scattering is smaller than those obtained based on Mie theory, for microwave propagating in charged sand/dust atmosphere. The smaller the atmospheric visibility, the greater the difference, while the difference decreases gradually as the atmospheric visibility increases. Then, it is more reasonable to consider multiple scattering attenuation at lower atmospheric visibility. When sand/dust particle is charged, the specific attenuation is increased, however, this increase is not linear. According to quantum illumination radar theory, a beam splitter-based optical link model is used to simulate the sand/dust atmospheric channel. The effects of charged sand/dust atmosphere with different visibility on the detection error probability, signal-to-noise ratio, and maximum detection range for microwave quantum illumination radar are studied by using quantum radar equation and quantum detection error probability theory. The performances of QI and classical two mode noise (TMN) radar are compared and analyzed. These results show that the performances of quantum illumination radar are improved with increasing sand/dust atmospheric visibility. When sand/dust particle is charged, the performances for QI radar are degraded due to attenuation increase. The change in the performances is nonlinear as the change in sand/dust carrying charge quantity. Increasing the signal frequency can enhance the performance of quantum illumination radar when visibility is high, but when visibility is low, the gain from frequency increase is outweighed by the performance degradation caused by increased attenuation, making it inadvisable to raise the frequency in such cases. The comparison with classical radar reveals that QI radar performs better under the condition of lower atmospheric visibility and lower average photon emission, but this advantage diminishes as the number of photons increases. In a word, these results show that the performances of QI radar are more significant at lower atmospheric visibility. Under higher visibility conditions, the QI system SNR can be improved by increasing frequency. The maximum detection range of the QI radar is significantly better than the classical TMN radar.

The numerical simulation of particulate reinforced composites by using a two-dimensional VCFEM formulated with plastic, thermal, and creep strain

The working environment of industrial fields where the Particulate Reinforced Composites (PRCs) are commonly used is complex. In these harsh environments, PRCs produce some nonlinear strains, such as plastic, thermal, and creep strains, which affect the performance of PRCs to some extent. In addition, the actual structure of PRCs contains a massive amount of inclusions, and the number of inclusions could reach the magnitude of millions or even tens of millions. For the large-scale numerical simulation of PRCs, ordinary displacement finite element requires a large number of meshes, which increases their computational complexity and unsolvability. For real PRCs with random particle distribution, homogenisation theory does not accurately capture the true state of stress concentration in PRCs. The Voronoi cell finite element method (VCFEM) can be a good solution to the drawbacks of the above two numerical simulation methods. The subject of the present work explores a modified complementary energy generalized function that takes the constitutive relationship of nonlinear strain into account, proposing a two-dimensional VCFEM, formulated with plastic, thermal, and creep strain. The simulation results are compared with the computed results of a commercial finite element software Marc to verify the validity and accuracy of the VCFEM. Results of the comparison show that the VCFEM has the advantages of simple meshing and faster computation under the same precision. The VCFEM, considering plastic, thermal, and creep strains proposed in this paper, lays the foundation for future large-scale numerical simulations of PRCs containing a large quantity of inclusion structures and can realistically capture the stress concentration state of PRCs. At the end of the article, the impact of the order of stress function on the calculation results is discussed.

High-throughput sorting of nanoparticles with light-patterned dielectrophoresis force

We present a size-based sorting method for nanoparticles in microfluidics with the aid of light-patterned dielectrophoresis (DEP) force. In a microfluidic channel, we have succeeded in manipulating a random distribution of particles into a single stream with the DEP force as well as the hydrodynamic force, and more strikingly, the trajectory of particles is found to be size-dependent, implicating that we can precisely separate nanoparticles based on their sizes even if they are identical in mass. We have numerically predicted the behavior of sorting nanoparticles, emphasizing on the size, velocity and electrical permittivity, so as to know their influences on the effective sorting, particularly in terms of high throughput. Our work confirms that what we believe to be the novel manipulation of nanoparticles features its flexibility as well as high throughput.

Maxwell homogenisation methodology for evaluation of effective elastic constants of weakly-nonlinear particulate composites

The far-field methodology, developed by J. C. Maxwell in 1873 for the evaluation of the electrical conductivity of a particle-reinforced material, is utilised to estimate the effective third order elastic constants of composite media containing a random distribution of spherical particles. The results are explicit analytical relationships derived using a perturbation solution, that are in a broad agreement with previous studies employing different homogenisation schemes. In contrast to other homogenisation schemes, Maxwell's methodology relies only on the far-field asymptotics. Therefore, the methodology may be more suitable for the evaluation of effective mechanical and physical properties of composites where the far-field asymptotics are known or can be derived. The main fundamental result of this work is the first demonstration that the Maxwell methodology may be applied to weakly nonlinear problems. Therefore, it can offer a new, simple method to analyse homogenisation problems in many other fields.

Uncertainty and statistics of dislocation-oxide interactions on strength and creep failure of oxide dispersion strengthened steels

Oxide dispersion strengthening (ODS) steels exhibit excellent mechanical and creep properties due to the nanoscale oxide particles as barriers to dislocation motion, for the great application potentials to structural components of advanced nuclear power plants. However, the classical ODS model described by the Orowan mechanism or cutting mechanism only depends upon a single average size, ignoring uncertainty and statistics of dislocation-oxide interactions induced by the spatial and dimensional random distribution of oxide particles. When the oxide particle size is larger than the critical size to determine the interaction mechanism transformation from cutting to looping, the Orowan mechanism and cutting mechanism would exist simultaneously. Here we propose a modified ODS model for strength and creep rate considering the spatial distribution and size distribution of oxide particles, which is more consistent with the experimental result compared to the classical ODS model. In addition, the contribution of oxide particles to the yield strength and creep strength is evaluated quantitatively for various precipitate sizes and volume fractions. It is believed that this model provides an analytical framework to accurately predict the strength and creep rate of ODS steels.

Correlation between meso-structures and magnetoelectric properties in 0-3 magnetoelectric composites

The quantitative characterization of macroscopic properties of 0-3 polymer-based magnetoelectric (ME) composites is the basis of their application, and the meso-structure is the source that affects their macroscopic properties. In this study, theoretical models consistent with the meso-structures of the composites are developed to address scenarios involving random, chain, and agglomeration particle distributions. The focus is on investigating the nonlinear mechanical behavior of multi-field coupling. The validity and rationality of the theoretical model are confirmed by comparing the theoretical results with existing experimental data. Based on this, the distribution characteristics of elastic field, electric field and magnetic field of 0-3 polymer-based ME composites with different particle arrangements are analyzed. Additionally, the influence of several factors on the ME properties, such as magnetic field strength and direction, particle proportion and frequency, is discussed. The results indicate that the ME performance is optimized when the direction of the densest chain row of particles aligns with the direction of the applied magnetic field. Furthermore, the phenomenon of current leakage is examined when the direction of the chain row of ferromagnetic particles coincides with the direction of polarization. To mitigate this leakage effect, a solution is proposed by adding thin layers of piezoelectric polymer to the surface of the composite, resulting in a 62.6% increase in ME performance. The research findings reveal the response mechanisms and essence of 0-3 polymer-based ME composites under applied magnetic fields, establishing a connection between meso-structures and macro properties. This provides guidance for the design and optimization of mesomorphic structures and performance, and serves as a foundation for the design, fabrication, and development of a new generation of ME devices.

Predicting the particle-agglomeration effect on the equivalent mechanical properties of dispersion nuclear fuel by machine learning

The optimal design of dispersion nuclear fuel necessitates precise correlations between the particle distribution and mechanical properties, which can be established by combining the numerical method, data-driven technique, and machine learning model. In this study, we developed an automated high-throughput workflow to rapidly generate massive number of dispersion nuclear fuel meat models with different particle distributions, and further built the machine learning database for predicting the crucial mechanical properties. Effects of the particle-agglomeration behavior on mechanical properties of the dispersion fuel meat were analyzed in length. The automated workflow includes the entire parametric modeling, parallel computation and post-processing of simulated results, through which we can calculate the equivalent elastic modulus and the maximum Mises stress of the dispersed microstructure with randomly distributed fuel particles. The Fourier distribution function is utilized to characterize the random particle distribution configuration. The analysis suggests that the particle distribution configuration, the volume fraction and the number of agglomerate particles are the crucial factors determining the performance of dispersion nuclear fuel. Furthermore, through utilizing the database constructed by high-throughput computing, the Gaussian process regression algorithm was successfully applied to accurately forecast the mechanical properties in the dispersion fuel meat. This work lays a foundation for optimizing the design of high-performance dispersion nuclear fuel, quickly estimating mechanical properties of composite structures containing randomly dispersed particles, and further extending to analogous systems.

Hydrophobicity-driven self-assembly of nanoplastics and silver nanoparticles for the detection of polystyrene microspheres using surface enhanced Raman spectroscopy

Microplastics (MPs) and Nanoplastics (NPs) accumulated in the environment have been identified as a major global issue due to their potential harm to wildlife. Current research in the detection of MPs is well established. However, the detection of NPs remains challenging. The aim of this paper is to investigate the detection of polystyrene (PS) NPs on a super-hydrophobic substrate using surface-enhanced Raman spectroscopy (SERS) technology after high-speed centrifugation of PS NPs and AgNPs. The hydrophobic substrate reduces the contact area of droplet, concentrating PS NPs and AgNPs on a small spot, which eliminates the random distribution of nano particles. The condensed PS NPs and AgNPs improve the SERS intensity, reproductivity and detection sensitivity. The results show that SERS measurement on a hydrophobic substrate could significantly improve the detection sensitivity of PS NPs, with the detection limits of PS NPs as low as 0.5 mg/L (500 nm PS NPs) and 1 mg/L (100 nm PS NPs). The study provides an effective and rapid method for the detection of NPs at trace concentration, demonstrating more possibility for the future detection of trace NPs in the aquatic environment.