Model Of Mixture Research Articles

Study of a Toroidal Lens

This letter presents a study of a toroidal-shaped lens following a Luneburg-like distribution of refractive index intended to multibeam radio frequency front ends. Due to its toroidal shape, this is a new model of lens that does not present any blind spot, as an opposite to any other kind of either spherical or cylindrical lens when adding both antennas and associated electronics. A sidelobe level reduction is proposed based on interface intrinsic impedance matching with magnetic–dielectric composite materials. It also proposed a set of numerical approximations of the main electromagnetic parameters, such as the half-power beamwidth in both the E- and H-planes, and the sidelobe level, and the directivity, in order to provide some basic guidelines for such a lens project. Given the new shape, the impacts of the curvature of the torus on the radiation pattern are also carried out. In order to prototype a toroidal lens with 3-D printing technology, a material has been fully characterized electromagnetically considering infill ratio variations in some effective dielectric constant models for multimaterial mixtures. The resulting toroidal lens has been characterized in far field, demonstrating a good agreement with the model and confirming its great potential for multibeam applications.

Effects of Literacy Environment and Quantity and Quality of Reading Interaction on Preschoolers’ Early Literacy Skills: Latent Profile Analysis

본 연구의 목적은 음운론적 인식, 어휘력, 이야기 이해력을 토대로 유아의 기초문해력을 유형화하고, 가정의 물리적 문해환경과 책 읽기 상호작용이 유아의 기초문해력 유형에 영향을 미치는지 검증하는 것이다. 이를 위해 만 4세 유아와 어머니 125쌍을 대상으로 유아의 기초문해력 유형화를 위해 잠재프로파일 분석을 실시하고, 유형에 따른 차이를 확인하기 위해 일원배치분산분석, 다항로지스틱 회귀분석을 실시하였다. 연구문제에 따른 분석 결과는 다음과 같다. 첫째, 기초문해력의 유형은 ‘평균’, ‘지연’, 음운론적 인식과 어휘력이 높은 ‘우수’ 집단으로 구분되었다. 둘째, 가정의 물리적 문해환경은 ‘평균’과 ‘우수’ 집단이 ‘지연’ 집단보다 풍부했다. 질적 책 읽기 상호작용은 ‘평균’ 집단이 ‘지연’ 집단보다 풍부했다. 셋째, 질적 책 읽기 상호작용의 수준이 낮을수록 ‘평균’ 집단보다 ‘지연’ 집단에 속할 가능성이 높았고, 물리적 문해환경이 풍부할수록 ‘지연’ 또는 ‘평균’ 집단보다 ‘우수’ 집단에 속할 가능성이 높았다. 이러한 결과는 가정의 물리적 문해환경과 질적 책 읽기 상호작용이 유아의 기초문해력 향상에 중요한 요인임을 보여준다. 본 연구는 유아기 자녀가 있는 가정 대상 부모교육 및 중재 프로그램 개발에 활용 될 수 있을 것이다.This study aims to identify the profile of early literacy skills based on phonological awareness, vocabulary knowledge, and story comprehension and verifies the effects of home literacy environment and reading interaction on early literacy skills. Participants of this study were 125 pairs of four-years-old preschoolers and their mothers. Latent profiles analysis was conducted to classify children into different groups according to levels of early literacy skills. One-way ANOVA and multinomial logistic regression was conducted to verify the differences between groups. First, we identified three different groups of early literacy skills: average, delayed, and excellent groups. Second, children in the excellent group showed higher levels of phonological awareness and vocabulary knowledge when compared to children in the average and delayed groups. Home literacy environment was richer in the average and excellent groups than the delayed group, whereas quality of reading interaction was higher in the average group than the delayed group. Third, lower quality of reading interaction predicted the higher possibility of classification into the delayed group rather than the average group and richer home literacy environment predicted the higher possibility of classification into the partially excellent group rather than the delayed and average groups. This indicates that home literacy environment and quality of reading interaction are important to enhance preschoolers’ early literacy skills. The results of this study can be used to develop parent education or a child intervention program for preschoolers and their parents.

Towards an improved prediction of asphalt pavement rutting through thermal-mechanical coupled constitutive modelling

ABSTRACT To predict accurately the evolution of asphalt pavement rutting, an advanced numerical modelling process was proposed and successfully implemented through the finite element software ABAQUS. In the process, a thermal-visco-elastic-plastic constitutive model for asphalt mixture was derived into numerical form and compiled using the user-defined material subroutine (UMAT). To apply realistic traffic loading, a mathematical form of loading model was proposed according to the tire-pavement interaction and realised through the user-defined load subroutine (DLOAD). Besides, a heat transfer model was calculated before the mechanical calculation, and the results of the pavement temperature field were used as the boundary condition for the mechanical calculation. A few case simulations were conducted using the numerical process and the results indicate that the severe rutting at pavement intersections or parking lots is due to not only the longer loading time but also the deeper plastic deformation region. In addition, the numerical model is capable of quantitatively identifying the critical temperature at which the pavement rutting starts to grow rapidly; accordingly, a new criterion for taking strategies to avoid or slow down the rutting distress of the pavement can be proposed, which is of great significance for the practice.

A micromechanical investigation of sand–rubber mixtures using the discrete element method

Mixing soil or sand with tire rubber fibers or buffings is a practical and promising solution to the problem of global scrap tire pollution. However, sand–rubber mixtures exhibit unsatisfactory and complex mechanical properties in engineering applications due to the large deformation of rubber fibers. In this study, a detailed numerical approach to modeling mixtures of sand and rubber fibers via the discrete element method (DEM) was proposed, and the effect of rubber content on the macroscopic and microscopic mechanical behaviors of sand–rubber mixtures was investigated. Furthermore, the reinforcing mechanism by which rubber fibers contribute to the micromechanics of sand–rubber mixtures was explored to determine the optimum rubber content in terms of soil mechanical performance. Comparative analysis of the experimental and numerical results demonstrated the applicability and ability of the proposed DEM model and related parameters for modeling sand–rubber mixtures. Through investigation of the constitutive behaviors of sand–rubber mixtures with various rubber contents under triaxial compression, a rubber content of 20% was found to provide the best shear resistance in the critical state. The micromechanics of sand–rubber mixtures, namely particle kinematics, the interparticle coordination number and rubber fiber deformation, were investigated to demonstrate the specific reinforcing mechanism of rubber fibers with respect to improved soil performance. The resulting data strongly support the identified optimum rubber content for sand–rubber mixtures that will provide a valuable guidance to the relevant engineering applications.

Thermal buckling and vibration analysis of rotating porous FG GNPs-reinforced Reddy microplates

Thermal buckling and free vibration analysis of rotating functionally graded (FG) graphene nanoplatelets (GNPs) reinforced nanocomposite porous metal-matrix microplates subjected to a linear thermal gradient are presented in this paper. The third-order shear deformation theory (TSDT) of Reddy and the Lagrange's equation of the second kind are adopted to derive the governing equations of motion via the modified couple stress theory (MCST). Different patterns of porosity distribution and GNPs dispersion are considered. The effective material properties of the microplate are determined by employing the Halpin-Tsai micromechanical model and rule of mixture. Galerkin method is chosen as a numerical technique, in which the comparison functions for displacements are approximately described as the boundary functions multiplied by Chebyshev polynomials, to solve the governing equations for various boundary conditions. The accuracy and effectiveness of the present results are validated by comparing with the previous literature. The effects of GNPs dispersion pattern, geometry and weight fraction as well as porosity coefficient, porosity distribution, angular velocity and material length scale parameter on the critical buckling temperature rise and fundamental frequency are studied. It is contrary to expectations that either increasing the amount of GNPs or increasing the porosity coefficient improves the thermal buckling behavior. Results also show that the GNPs' performances are affected not only by their geometry, but also by the centrifugally stiffening and small-scale effects. Rotating motion weakens the enhancement of GNPs on the thermal buckling behavior. More clamped boundaries enhance the critical buckling temperature rise and fundamental frequency. The outcomes of this work can be used as the reference for future applications in various fields of industry and technology.

Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure–activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R 2) of 0.70 versus an R 2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures. Communicated by Ramaswamy H. Sarma

Parameter modification and extension of the compressible packing model (CPM) for steel fiber-aggregate mixtures

With the popularization of steel fiber reinforced concrete (SFRC), research on the packing of steel fiber-aggregate (F-A) mixtures has become a significant prerequisite for the mix design of SFRC. Although the compressible packing model (CPM) can be used to predict the packing density (PD) of F-A mixtures, its interaction coefficients are determined from the elementary particle classes without fibers (narrow particle size distribution), which limits the applicability of the CPM. In this study, after adding steel fibers to binary aggregate with wide particle size distribution, the PDs of steel fiber-binary aggregate (F-BA) mixtures were measured. The method of establishing interaction coefficients for F-A mixtures was modified, and the general formulas of interaction coefficients were proposed. The novel CPM model after comprehensive corrections of fiber disturbance parameter and interaction coefficients for F-A mixture was established. Compared with the conventional CPM, the prediction accuracy of novel CPM was greater.

Singular limits of binary mixtures in solids theory

We investigate the long-term behavior, as a certain coupling parameter α becomes large, of a one-dimensional model of a binary mixture of solids with nonlinear damping and nonlinear feedback forces. We prove the existence of a smooth global attractor with finite fractal dimension and study its limiting properties when α tends to infinity. More precisely, we prove that this limit coincides with a one-dimensional single wave equation. Finally, we also prove convergence of the global attractor of the binary mixture model to the global attractor of the single wave equation as α→∞. To the best of our knowledge, this result is new for singularly perturbed systems of a binary mixture of solids.

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models. We illustrate the different options with examples from recent research and give an outlook on future developments.

Monitoring adherence and abstinence of cannabis use disorder patients: Profile identification and relationship with long-term treatment outcomes

BackgroundPatients with cannabis use disorder (CUD) show heterogeneous sociodemographic and consumption patterns. Although previous studies, focused on identifying subgroups of CUD patients using input variables, have yielded useful results for planning individualized treatments, no published research has analyzed the profiles of CUD patients according to their therapeutic progress. This study therefore aims to identify subgroups of patients using adherence and abstinence indicators and to explore whether these profiles are associated with sociodemographic characteristics, consumption variables, and long-term therapeutic outcomes. MethodsThis was a retrospective observational study with a multisite sample of 2055 CUD outpatients who were beginning treatment. The study monitored patient data at two-year follow-up. We conducted latent profiles analysis on the appointment attendance ratio and percentage of negative cannabis tests. ResultsA three profile solution emerged: i) moderate abstinence/moderate adherence (n = 997); ii) high abstinence/moderate adherence (n = 613); and iii) high abstinence/high adherence (n = 445). The study found the most marked differences at the beginning of treatment for education level (chi2 (8) = 121.70, p < .001), source of referral (chi2 (12) = 203.55, p < .001), and frequency of cannabis use (chi2 (10) = 232.39, p < .001). Eighty percent of patients from the “high abstinence/high adherence” group were relapse-free at two year follow-up. This percentage decreased to 24.3 % in the “moderate abstinence/moderate adherence” group. ConclusionsResearch has shown adherence and abstinence indicators to be useful for identifying subgroups of patients with different prognoses regarding long-term success. Recognizing the sociodemographic and consumption variables associated with these profiles at the beginning of treatment could help to inform the design of more individualized interventions.

Long-term skid resistance and prediction model of asphalt pavement by accelerated pavement testing

To achieve the optimal design and accurate prediction for the long-term skid resistance performance of asphalt pavement, the skid resistance and its evolution trend prediction models of different asphalt mixtures were studied based on the accelerated pavement test using a small accelerated loading device (MLS11). First, three oil-stone ratios, three gradations and four types of coarse aggregates were prepared for different types of asphalt mixes. Second, the long-term skid resistance degradation characteristics and its influencing factors of different asphalt mixtures were studied based on the indoor accelerated pavement test using MLS11. Next, the macro-structure and micro-structure for the evolution prediction models of asphalt pavement were constructed and validated based on the combination of the numerical model and test section. Finally, the design standard and evaluation indexes for skid resistance of high-grade asphalt pavements under heavy traffic conditions were proposed. The results show that dense graded asphalt mixes with a smaller oil-stone ratio have better skid resistance, the type of gradation has less influence on its skid resistance, and the mixture consisting of aggregates with a richer mineral composition type and large difference in mineral hardness has better durability of skid resistance. In addition, the prediction models for the macro-structure and the micro-structure were established, respectively, and the errors of MTD and BPN measured in the test section were controlled within 4% and 5% of the theoretical values. The attenuation rate |A|≤2.15 is proposed as the design criterion for the skid resistance of asphalt pavements.

Repartitioning-based aggregate generation method for fast modeling 3D mesostructure of asphalt concrete

A repartitioning-based aggregate generation method is proposed to establish the three-dimensional mesostructure models of asphalt mixture. In this method, a stepwise partitioning procedure is proposed to repeatedly partition the specimen space into Voronoi cells with high variation in shape and size. The number of the Voronoi cells is determined by dividing the volume of the specimen space by the volume of a so-called equivalent sphere; in this way, the sizes of the generated Voronoi cells can be controlled around the sizes of the equivalent spheres. Also, a reshaping-classifying-picking procedure is developed and applied to the Voronoi cells to obtain very realistic aggregates with expected gradations to form final mesostructures. The effectiveness of the repartitioning-based aggregate generation method was validated by using the aggregate gradation and complex modulus test data of asphalt concrete. Compared with several recent aggregate generation methods, the proposed method can faster generate three-dimensional mesostructures with large dimension and high aggregate content. Besides, it can explain quantitatively the effects of mesostructural properties on macromechanical characteristics of asphalt concrete, thus providing an efficient and robust tool for advancing material design of asphalt concrete.

Bayesian model for sustainable, resilient, and cost-effective concrete mixtures

ABSTRACT This work develops a Bayesian model that can be used to proportion sustainable, resilient, and cost-effective concrete mixtures based on stakeholder preferences. Traditionally, the decision-making process for proportioning concrete mixtures is centered around meeting target specifications, such as slump and compressive strength, and attributes such as sustainability and durability are given minimal importance. However, in the global push towards green infrastructure, sustainability plays a major role and must be considered to the same degree as engineering attributes. The model developed herein accounts for embodied carbon footprint, cost, and time to corrosion initiation, apart from the target specifications commonly used to proportion concrete mixtures. The developed model is demonstrated through two case studies: one targeting the average industrial embodied carbon footprint for a concrete mixture and the other targeting a reduction of 40% from the industry’s carbon footprint average. This model can also be used to perform data visualization and sensitivity analyses.

Thermodynamic properties, viscosity modelling and FTIR studies of binary liquid mixtures of 2-isopropoxy ethanol with amines at different temperatures

Thermodynamic and spectral analysis for binary liquid mixtures of 2-isoporopxy ethanol with diisopropylamine, di-n-butylamine and tri-n-butylamine has been carried out in the present discussion. For this purpose, measurements of density, speed of sound and viscosity for binary mixtures were carried out over the entire composition range and at temperatures (298.15, 303.15 and 308.15) K. Various excess and deviation parameters were evaluated using the measured properties. Behaviour of calculated parameters was found quite sensitive towards the intermolecular interactions. Change in behaviour of these parameters with composition and temperature has been discussed in terms of intermolecular interactions due to physical and chemical effects among unlike molecules. Excess parameters were correlated with composition using Redlich-Kister polynomial. Existing theories and semi-empirical relations for viscosity were tested. Spectroscopic methods were also utilized to support the results obtained from above calculated parameters qualitatively.

Recycled asphalt mixture's discrete element model-based composite structure and mesoscale-mechanical properties

To investigate the composite structure and mesoscale-mechanical properties of recycled asphalt mixture, this paper has established discrete element models of recycled asphalt mixture with distinct characteristics of new aggregates, recycled asphalt pavement, new asphalt, and old asphalt according to the coarse aggregate contour characteristic parameters obtained by three-dimensional scanning. The composite skeleton and micro-crack development characteristics of recycled asphalt mixture were also studied. Finally, a method for determining the recycled asphalt pavement content was proposed considering the balance between rutting and crack resistance. The results show that the recycled asphalt pavement content has a negligible impact on the number of contact points of the composite skeleton, and this component primarily impacts the interfacial mechanical properties and contact of asphalt mortar. When the recycled asphalt pavement contents were 0 %, 15 %, 30 %, 45 %, and 60 %, the bottom stresses of the rutting specimens were approximately 0.6, 0.55, 0.5, 0.48, and 0.44 times of the bearing plate stress, respectively. In the composite skeleton, there were compressive, tensile, and tensile–compressive stresses at the contact point of coarse aggregates, the proportions of which were generally 70–80 %, 15–20 %, and 5–10 %, respectively. Shear cracks mainly occurred in the recycled asphalt pavement contact interface area, while tensile cracks occurred at the aggregate–aggregate contact interface, as well as in the asphalt mortar. The number of shear cracks was 50–80 % that of the tensile cracks. The maximum micro-crack length is 8–12 mm, and its minimum length is approximately 0.3 mm with micro-crack generally in the asphalt mortar. The results can provide a reference for the recycled asphalt mixture design to improve road performance.

An improved regression model to predict fatigue life of asphalt mixes incorporating low to moderate RAP contents

The research on the fatigue performance of recycled asphalt mixture and the establishment of its fatigue life prediction model is the key to its large-scale engineering application. The four-point bending fatigue tests of the fresh and recycled asphalt mixtures under the strain control of cyclic loading were conducted to investigate influencing factors on their fatigue performance and establish a new fatigue prediction model for RAP (recycled asphalt pavement). The hot recycled asphalt mixture samples of AC-16C were prepared by a uniform test design method, with the RAP content of 0%, 20%, 30%, and 40%. Through the fatigue test data of 20 groups of fresh asphalt mixture with 0% RAP and 40 groups of recycled asphalt mixtures with the contents of 20%, 30%, and 40% RAP, the simplified applicability of the fatigue prediction model in the current Chinese specifications for the design of highway asphalt pavement (JTG D50-2017, JTG model) and its parameters modification were studied, thereby an improved regression fatigue prediction model of recycled asphalt mixture is established on consideration of strain level, asphalt content, void ratio, and RAP mixing amount. This fatigue prediction model for recycled asphalt mixtures was proposed by modifying the parameters of the fatigue equation for new asphalt mixtures in the Chinese Highway Asphalt Pavement Design Code. (JTG D50-2017) and comparing the fatigue test data of the recycled asphalt mixture. The research results show that: The simplified JTG fatigue prediction model can effectively predict the fatigue life of fresh asphalt mixture with the content of 0% RAP, and the model parameter a is a constant of the order of 1016; The correction coefficients α and β of the initial bending stiffness modulus S0 and the Voids Filled with Asphalt (VFA) in the improved JTG model of the hot recycled asphalt mixture considering the RAP content are 0.006 and 0.0136, respectively; The model can accurately predict the fatigue life of recycled asphalt mixtures with the range of RAP content of 0–40%, with an average deviation of only 0.106, and has higher prediction accuracy for fatigue life measured close to or higher than 106 times. The average deviation of the predicted life of 20%, 30%, and 40 %RAP fatigue is only 0.057, 0.079, and 0.125. This paper’s indoor fatigue prediction model has higher accuracy for recycled asphalt mixture than the well-known traditional models.

CALCULATION OF THE MELTING POINTS OF ALKALI HALIDES USING THE THERMODYNAMIC PERTURBATION THEORY

A model for calculating phase equilibria between a liquid and a crystal is proposed, which makes it possible to evaluate the melting points of ionic compounds. The dependence of the melting temperatures of alkali halides on the cation-anion composition can be described in terms of ionic radii and polarizabilities using thermodynamic perturbation theory for the molten phase. The chemical potential of the crystal phase contains the Born-Mayer formula for the electrostatic part of the energy and the Debye formula for the vibration contribution. The full system of equations describing the equilibrium between liquid and solid includes not only the equality of chemical potentials, but also contains the equation of state to calculate the equilibrium density of melts at the crystallization point. One more equation of the system is necessary for the self-consistent computation of the characteristic Blum’s screening parameter within the mean spherical model of the ionic mixture. On this basis, the melting points of fluorides, chlorides, bromides and iodides of lithium, sodium, potassium, rubidium and cesium have been calculated. It has been shown that the combination of the reference mean-spherical model of charged hard spheres with different diameters and the perturbation due to the charge-induced dipoles into the chemical potentials of molten salts is a good basis for quantitative agreement with experimental data on the melting temperatures within a few percent. Moreover, the regularities of the change in the melting temperatures reduced to the Coulomb energy at the maximum contact of the cation and anion, as well as depending on the difference in the ionic radii of the salts, are discussed.

Particleboard experimental production with bamboo, pine and mate for one product of new applications

Particleboard can be produced from a mixture of different lignocellulosic materials, which can be chosen depending on the density required for the panel and its applications. The aim of this study was to evaluate the mechanical properties of particleboard with bamboo, pine and mate for a new product of high density for the special applications currently served by HDF. Particles of bamboo (Phyllostachys aurea) finely chopped sticks of mate (Ilex paraguariensis) and commercial particles of southern pine (Pinus taeda) wood were used. These particles used 100 % by weight in the panel or in mixtures of 50 % each (three mixtures) or in a triple mixture of one third each, were glued in a drum-type rotary mixer with melamine-urea-formaldehyde (MUF) resin, and pressed in hydraulic press at 120 °C and 5,88 MPa for 10 minutes, up to 6 mm thickness. The panels were produced with 900 kg/m3 nominal density and, after pressing, were conditioned at 20 ºC and 65 % relative humidity. Statistical was performed by means the variance analysis and simplex centroid experimental design, with three replicates. It was found the use of pine particles contributed mainly to increase the panel's strength and stiffness, while the use of mate particles facilitated internal bond strength. The mixtures modeling showed that the water absorption, the strength and stiffness in bending and the internal bond strength are explained by the cubic model, while the thickness swelling and hardness are explained by the quadratic model. The best physical and mechanical properties results were found for the pine, bamboo and mate same ratio mixture.

Evaluation of Steel Slag Optimal Replacement in Asphalt Mixture under Microwave Heating Based on 3D Polyhedral Aggregate Electromagnetic-Thermal Meso-Model

Replacing conventional aggregate with steel slag waste can boost the microwave absorption properties of asphalt mixtures and reduce pollution to protect the environment. In order to achieve the best healing in steel slag asphalt mixture, the optimum particle size and content of steel slag are essential. For this purpose, a high-efficiency algorithm for the random growth and placement of convex polyhedron aggregate is proposed in this paper. The limestone aggregate is replaced with an equal volume of steel slag, and a three-dimensional mesoscale random model of steel slag asphalt mixture is developed. The process of microwave heating is simulated by FEM. The numerical simulations are compared with the reported experimental data, which proves that the model is reliable (R2 = 99.40%). Both the volume average temperature and the uniformity of temperature distribution indicate that the steel slag replacement rate of 60% at 4.75–9.5 mm and 60% at 9.5–13.2 mm is optimal, among which the heat transfer of 4.75–9.5 mm steel slag is more uniform, and the temperature gradient is lower. Steel slag can dramatically increase the heating rate of an asphalt mixture, and the peak of the temperature gradient is around the boundary of steel slag. The reflection properties of steel slag may be related to the dielectric constant, permeability, and particle size. Excess steel slag will cause overheating in most zones of the specimen and will also depress the absorption efficiency of microwaves. The coefficient of variance for spherical (0.36) and polyhedral (0.32) aggregate specimen temperatures indicates that the aggregate’s shape has a negligible effect on the heat transfer of asphalt mixtures.

Generalised Kelvin contact models for DEM modelling of asphalt mixtures

ABSTRACT Rigid particle models based on the discrete element method (DEM) have been adopted to model creep, fracture, and the viscoelastic behaviour of asphalt mixtures considering an irregular micro-structure and particle contacts. Within a DEM framework, the Burgers contact model, which is known to have a narrow frequency and temperature range, is usually adopted to model viscoelastic properties. In this study, a new explicit three-dimensional generalised Kelvin (GK) contact model formulation for the DEM model is proposed for asphalt materials. The model is implemented following two different methodologies (GK 1 and GK 2 ). The models are validated in uniaxial tension-compression sinusoidal tests for predicting the dynamic modulus ( | E ∗ | ) and phase angle (ϕ) of these composites at a frequency range of 1–10 Hz at 20 ∘ C. Four mixtures are investigated based on the modelling of their mastic. The influence of the GK contact parameters on the dynamic response of mastics is assessed. Results show that κ m has an important influence on both rheological properties and that η m can be used for small adjustments focussing on the predicted phase angle. Moreover, it is shown that a viscoelastic contact model should be adopted to simulate aggregate-to-mastic contacts in mixtures. As expected, the response obtained for both GK models is the same but the simulations with the GK 1 are 14% faster. In addition, the response predicted with the proposed GK contact model is compared with the response predicted with a Burgers contact model. The DEM predictions obtained for the GK model are more accurate. For mastics, the average errors for | E ∗ | and ϕ when adopting the GK model (Burgers) are 2.40% (3.46%) and 3.64% (4.17%), respectively. For mixtures, the average errors for | E ∗ | for the GK model (Burgers) are 7.00% (7.92%). The proposed contact model greatly enhances the DEM ability to simulate the viscoelasticity of asphalt materials.