Representative Volume Element Research Articles

A scale-span method to characterize the mechanical property of BCF/PEEK considering uncertain structural characteristics

The focus of this study is exploring a scale-span characterization method to predict the mechanical properties of Braided Carbon Fiber Reinforced Poly Ether Ether Ketone (BCF/PEEK) considering structural uncertainty. Firstly, a complicated micro-scale Representative Volume Element (RVE) model that considers the random position and size of fiber was established via the developed Python script. Analogously, a mathematical expression adopted by the trust region algorithm was proposed to accurately describe the detailed cross-sectional shapes and fluctuation amplitude characteristics of BCF/PEEK for the sake of establishing the precision meso-scale RVE model. Then, the corresponding elastic properties that consider fiber volume fraction and fiber distribution location were characterized via the span-scale characterization method. Meanwhile, the influence of fiber bundle fluctuation amplitude and fiber volume fraction on the mechanical properties was investigated as well. In addition, the mechanical properties of BCF/PEEK with the change of the braid angle between warp and weft yarn were predicted via the established meso-scale RVE model. Finally, a series of experiments have been carried out. The maximum and minimum absolute prediction deviation of all elastic property parameters were only 4.07 % and 1.41 %, respectively, which verified the proposed scale-span characterization method can predict the mechanical properties of composites well.

Determining the Size of Representative Volume Elements for a Two-Dimensional Random Aggregate Numerical Model of Asphalt Mortar without Damage.

The size of the representative volume element (RVE) for the two-dimensional (2D) random aggregate numerical model of asphalt mortar in a non-destructive state, which directly affects the time required to simulate the linear viscoelastic behavior from asphalt mastic to asphalt mortar. However, in the existing literature, limited research has been conducted on the size determination of the numerical model RVE for asphalt mortar. To provide a recommended size for the typical 2D random aggregate numerical model RVE of asphalt mortar in a nondestructive state, this paper first applies the virtual specimen manufacturing method of asphalt concrete 2D random aggregate to asphalt mortar. Then, it generates numerical model RVEs of asphalt mortar with different maximum particle sizes, after which geometric and numerical analyses are conducted on these models. Finally, based on the geometric and numerical analysis results, the recommended minimum sizes of RVE for the 2D asphalt mortar numerical model are provided.

Analysis of rolling contact and tooth root bending fatigue in a new high-strength steel: Experiments and micromechanical modelling

To find materials that meet the future requirements of increasing load density in wind turbine gear structures, the commonly used reference material, 18CrNiMo7-6, and another high-strength steel were analysed. Their fatigue properties were studied through rolling contact fatigue (RCF) and gear tooth root bending fatigue (TRBF) tests. The reference steel exhibited better fatigue performance under RCF conditions compared to the new steel; however, it was outperformed by the new steel under TRBF conditions. This study aims to understand gear contact fatigue at the microscopic level, moving beyond the macroscopic focus that dominates current research literature. To establish the causal link between the varied fatigue performance observed in these materials, we proposed a multiscale modelling workflow based on crystal plasticity. This crystal plasticity framework is combined with the fatigue indicator parameter (FIP) to explore the fatigue resistance of materials subjected to RCF and TRBF conditions. Emphasis has been placed on the role of retained austenite (RA) in fatigue performance. Utilizing representative elementary volumes (REVs) generated based on statistically representative crystallographic features and measured size distribution of RA, we examined the effects of various RA levels on fatigue resistance. Our findings reveals that the fatigue damage accumulation is significantly influenced by the level of RA. Different fatigue damage accumulation behaviours were observed under RCF and TRBF conditions. These insights offer new perspectives on RA’s role in fatigue resistance and highlight its complex influence under varied loading conditions.

Multi deep learning-based stochastic microstructure reconstruction and high-fidelity micromechanics simulation of time-dependent ceramic matrix composite response

A multi deep learning-based framework is developed for efficient, automated microstructure reconstruction and generation of stochastic representative volume elements (SRVEs) with periodic boundary conditions (PBCs) for accurate modeling of ceramic matrix composite (CMC) response. The methodology comprises a convolutional neural network coupled with regression layers to act as a vanilla regression network for semantic segmentation of the microstructure, allowing accurate characterization of the phases and their distributions at the microscale. Scanning electron microscope and confocal microscope are used to obtain C/SiNC and SiC/SiNC CMCs micrographs for vanilla regression testing. Microstructure variability in terms of fiber volume fraction and porosity are quantified through the output regression layer, ensuring accurate representation of material variability in SRVE construction. Generative adversarial network (GAN) and its variants are designed to produce high-fidelity SRVE, spanning CMCs microstructure variability space. A circular padding algorithm is developed to generate SRVEs with PBCs during training of GANs. The accuracy of the generated SRVEs is established through micromechanics simulations, where an efficient formulation of the high-fidelity generalized methods of cells (HFGMC) approach is used to compute the effective mechanical properties. An iterative algorithm is implemented in the HFGMC solver to simulate time-dependent deformation of SiC/SiNC subjected to creep loading conditions.

Physically based modelling of orientation deviation effect on mechanical behavior for dual‐phase single‐crystal superalloy

AbstractThis work systematically investigates the orientation deviation effect on the elastoplastic deformation of a dual‐phase, nickel‐based single‐crystal superalloy through a combined experimental study and crystal plasticity finite element modelling method (CPFEM). Physically based, dual‐phase microstructural model was developed based on scanning electron microscopy (SEM), which was implemented by finite element (FE) modelling using a representative volume element (RVE) with periodic boundary conditions. An extended equivalent yield criterion coupled with CPFEM was adopted to describe the non‐uniform yield behavior induced by octahedral and cubic slip systems. The predicted results have shown that both the bulk behavior and localized stress–strain nature are orientation deviation dependent and that the first Euler angle plays a more important role in elastoplastic behavior than the second Euler angle. This study has thus advanced the basic understanding of the relationship between orientation deviation and the bulk deformation behavior of the dual‐phase nickel‐based single crystal.

Intra-yarn fibre hybridisation effect on homogenised elastic properties and micro and meso-stress analysis of 2D woven laminae: Two-scale FE model

In this paper, the effect of intra-yarn fibre hybridisation on the homogenised elastic properties and micro- and meso-scale matrix stress fields in 2D woven composite laminae (i.e. plain, 2/2 basket, 2/2 twill and 5-harness satin) is studied with a two-scale homogenisation scheme—employing a representative volume element model at micro-scale and a repeating unit cell model at meso-scale. The study is focused on S-glass/polypropylene/epoxy woven laminae with intra-yarn fibre hybridisation. A modified random sequential expansion algorithm generates microstructure for the micro-mechanical model, and a periodic meso-structure is used to generate the weave architecture for the meso-mechanical model. Both models are verified using analytical models. It is found that intra-yarn fibre hybridisation can significantly alter the homogenised properties as well as the micro- and meso-scale matrix stress fields—depending on the degree of hybridisation (i.e. the combination of S-glass and PP fibre volume fractions). Moreover, the homogenised lamina properties are found to be less sensitive to weave architecture and yarn thickness, but more so to the degree of intra-yarn fibre hybridisation, yarn width and yarn spacing. It is shown that the lamina properties can be tailored, and the micro- and meso-stress fields can be manipulated, by intra-yarn fibre hybridisation and weave architectures.

Enhanced multifunctionality in carbon fiber/carbon nanotube reinforced PEEK hybrid composites: Superior combination of mechanical properties, electrical conductivity, and EMI shielding

This study presents a novel approach to manufacturing carbon fiber and carbon nanotube-reinforced PEEK hybrid composites (CF/CNT/PEEK) using compression molding. The hybrid composite characteristics were compared with those of a control composite (CF/PEEK). Various tests were conducted on both composite types to investigate the impact of incorporating a carbon nanotube sock layer (an ultra-thin layer of a low-density CNT network), including three-point bend, electrical conductivity, and EMI shielding. The research also underscored the importance of EMI shielding simulation. An important aspect of the study involved performing multiscale simulations on control and hybrid composites, employing a digital twin methodology, and validating the results with experimental findings. For the hybrid composite, double multiscale simulations were performed by integrating two local-scale models, a CF/PEEK unit cell (micro-scale) and a CNT/PEEK representative volume element (RVE) (nano-scale), into a global-scale model. A detailed post-processing analysis of the simulation results was conducted to analyze stress distribution on local and global scale models. It was noted that there was an over 8 % increase in the flexural strength of the hybrid composite, along with a reduction of over 27 % in the standard deviation of the results. The introduction of CNTs enhanced the electrical conductivity by more than 36 %. It conferred remarkable EMI shielding effectiveness exceeding 50 dB across the entire X-band frequency range, attributed to enhanced reflection and absorption. EMI shielding simulations suggest that refining the manufacturing process of the hybrid composite could enhance its shielding properties. The developed multiscale simulation has been demonstrated as an efficient prediction tool, as the simulation outcomes closely align with experimental findings. The resultant hybrid composite is promising for potential multifunctional applications.

Multiscale Modeling of Elastic Waves in Carbon-Nanotube-Based Composite Membranes

A multiscale model is developed for vertically aligned carbon nanotube (CNT)-based membranes that are made for water purification or gas separation. As a consequence of driving fluids through the membranes, they carry stress waves along the fiber direction. Hence, a continuum mixture theory is established for a representative volume element to characterize guided waves propagating in a periodically CNT-reinforced matrix material. The obtained coupled governing equations for the CNT-based composite are found to retain the integrity of the wave propagation phenomenon in each constituent, while allowing them to coexist under analytically derived multiscale interaction parameters. The influence of the mesoscale characteristics on the continuum behavior of the composite is demonstrated by dispersion curves of harmonic wave propagation. Analytically established continuum mixture theory for the CNT-based composite is strengthened by numerical simulations conducted in COMSOL for visualizing mode shapes and wave propagation patterns.

Concurrent multiscale topology optimization for size-dependent structures incorporating multiple-phase materials

The significant progress in additive manufacturing has resulted in the development of well-engineered microstructures with innovative topological configurations, showcasing exceptional performance across diverse fields. Topology optimization is widely recognized as an advanced design tool that provides engineers with new insights for creating lightweight structures. As integration devices continue to trend towards miniaturization, the size effect in small-scale structures remains a persistent challenge. The absence of characteristic parameters to describe this size effect hinders proper explanation through traditional multiscale topology optimization based on classical mechanics. Therefore, we propose a size-dependent topology optimization framework for concurrent multiscale design involving multiple-phase materials. The modified couple stress theory is proposed to account for the size effect in small-scale structures. The numerical findings suggest that the size effect is significantly influenced by the ratio of elastic modulus, volume fraction, and width-to-length ratio of the representative volume element as its characteristic size approaches the length of the material. These results highlight the importance of considering size-dependent effects in topology optimization for multiscale design and provide valuable insights for engineers and researchers in this field.

A multiscale mechanics model for elastic properties of densified wood

We introduce a multiscale mechanics model for analyzing the elastic properties of super-strong densified wood (DW). Our model incorporates microstructural features such as microfibril angle and densification ratio, along with chemical parameters including degree of polymerization, crystallinity, and density of hydrogen bonds. At the nanoscale and microscale, the elastic properties of cellulose nanofibril and cell wall layers are derived analytically using the mechanics of composite materials. Finite element simulations based on representative volume elements are conducted at the mesoscale to obtain homogenized effective elastic properties at the macroscale. Our quantitative investigations validate that microstructural changes and alterations in chemical components significantly enhance DW's mechanical performance. Densification and chemical changes, especially increased cellulose content and reduced lignin, emerge as vital mechanisms for strengthening DW. The model's insights offer valuable guidance for optimizing the two-step preparation process of DW to achieve superior mechanical performance. Additionally, the versatility of the model allows for exploring the influence of cell dimensions and potential applications in designing bioinspired materials.

Study on mechanical properties of natural fiber (Jute)/synthetic fiber (Glass) reinforced polymer hybrid composite by representative volume element using finite element analysis: A numerical approach and validated by experiment

The excellent mechanical strength with low weight of polymer-based composites makes them superior over conventional materials like steel and aluminum in terms of mechanical properties. Nowadays, researchers are working to develop natural fiber-based sustainable composite. Glass fiber, when combined with jute fiber, could greatly enhance the mechanical characteristics of composites; the sequence of the layers mostly determines these properties. In this study, the hybrid laminate of jute and glass fiber with resin polyester is developed with The representative volume element utilized in ANSYS. In ANSYS material design module, the 3D microstructure of composite material is created with various fiber volume fractions of glass fiber and jute fiber. After that, the effective elastic properties of hybrid jute/glass fiber and polyester as matrix, glass fiber/polyester, and jute fiber/polyester are evaluated and the simulation-based effective elastic property is validated by existing experiment value which showed good agreement. For further study, in Ansys ACP PrepPost, the composite laminate such as hybrid jute + glass fiber/polyester, glass fiber/polyester, and jute fiber/polyester is created with desired lamina thickness, stacking sequence, and fiber orientation. Thereafter, it was transferred to a static structure module to perform tensile and bending test analysis. Ansys ACP post-processing is utilized to investigate stress induced at each lamina of the composite specimen. This study revealed that by adding glass fiber to natural fiber, the mechanical performance is enhanced significantly, whereas hybrid jute + glass fiber/polyester (S2) demonstrated the highest tensile strength 259 MPa compared to other hybrid laminate composites. Astonishingly, The hybrid composite jute + glass/polyester (S2) exhibited 10 % higher bending strength than glass fiber/polyester (S1) and 64 % more than net jute fiber/polyester (S5). Additionally, S2 demonstrated 9.5 % higher shear strength compared to S1 and 64 % more than S5. This study will give a great understanding of how adding glass fiber to jute fiber, could enhance the mechanical properties, which will contribute to the design of an efficient composite part for automotive such as car interiors, car doors, trim panels, lightweight applications, and packaging industries.

A comprehensive study of carbon nanoparticle shape and orientation effects on composite elastic properties using FEA

In this study, three critical parameters that influence the effective composite properties of Carbon nanoparticles were tested and modeled using finite elements through Digimat Finite Element Analysis (FEA) software, employing representative volume elements (RVE). The primary parameters under consideration are the fiber shape, fiber orientation, and fiber volume fraction. Four of the many different shapes of Carbon nanoparticles, namely Tubes, Disk, Sphere, and Cylinder, were investigated, coupled with four distinct orientations for tubes: horizontal, aligned at 45°, random, and vertical each varying in the carbon fiber volume fraction. The material of choice was pure epoxy resin because of the nature of the bonding between its two phases and its wide range of usage. Forces were applied along the X-axis, coupled with shear in the XY plane. This study revealed that the orientation of CNTs primarily influences the Elastic Young’s modulus, whereas the size of the CNTs has the main effect on the shear modulus, followed by orientation. The horizontal CNTs demonstrated superior Young’s modulus, but the random orientation had a more pronounced impact on the shear modulus variation. Regarding the analysis of the nanoparticles shapes, the disk shape with fixed orientation revealed the highest elastic modulus, after which the cylinder shape and the sphere recorded the minimum elastic modulus among the three studied shapes.

In search for representative elementary volume (REV) within heterogeneous materials: A survey of scalar and vector metrics using porous media as an example

The Representative Elementary Volume (REV) concept, a cornerstone in porous system heterogeneity assessment, was initially conceived to determine the minimal domain volume suitable for homogenization and upscaling. However, the definition of REV and usability in continuum-scale models is vague. In this study, we conduct comprehensive REV analyses on multiple samples, encompassing a range of scalar and vector metrics. Our investigation probes the representativity of crucial medium characteristics, including porosity, permeability, and Euler density, alongside descriptors rooted in pore-network statistics, correlation functions, and persistence diagrams. We explore both deterministic and statistical REV sizes (dREV and sREV), facilitating a robust comparative assessment. Crucially, we introduce an novel methodology tailored for harnessing vector metrics, known for their ability to reveal intricate structural insights. Our results underscore the superiority of the sREV approach, particularly for low-content metrics, addressing inherent limitations of dREV in characterizing homogeneities in such cases. Furthermore, the sREV approach incorporates stationarity analysis into REV evaluation, ensuring result consistency between sREV and dREV under stationarity conditions. Encouragingly, our findings suggest that high-information-content metrics, notably correlation functions combined with persistence diagrams, have the potential to establish a universal REV for steady-state physical properties. This proposition warrants further verification through a comprehensive assessment and comparison of REV values across major physical properties. REV analysis plays a pivotal role not only in assessing medium properties but also in scrutinizing different descriptors of 3D images – we note that REV analysis and image/field stationarity analysis are ultimately the same techniques under the hood. The discussion based on obtained results and recent finding by other researchers advances the understanding of REV within porous media, introduces a versatile methodology with broader applications, and is expected to be useful in numerous fields including materials science, cosmology, machine learning, and more. We redefine the classical definition of REV by adding stationarity condition and upper/lower bounds on its volume. While for simplicity, in this work we shall mainly focus on porous media as immediately applicable to digital rock, petrophysics, hydrology and soil physics problems, the developed mythology can be applied to other material types - composites, biological tissues, granular matter, food engineering and numerous other types of matter.

Correlating mechanical properties of Al/Al bimetal composite and its constituents using combined experiment and microstructure-based multiscale modeling approach

A combined experiment and multiscale modeling approach was presented to correlate the mechanical properties of a roll-bonded AA1050/AA3004 bimetal clad sheet and constituents. The mechanical responses of the 2-ply AA1050/AA3004 and constituents were measured under various loading conditions. For the measurements, each constituent was separated from the clad sheet using electrical discharge machining and uniaxial tensile tests. Continuum-scale yield functions such as isotropic von Mises and anisotropic/non-quadratic Yld2000-2d were developed for the two constituents and the bimetal clad sheet. A grain-scale crystal plasticity finite element (CPFE) simulations were also performed incorporated with newly developed representative volume element considering measured microstructural attributes. These CPFE predictions were used to identify the anisotropy coefficients for Yld2000-2d. Using the identified yield functions, finite element modeling was followed in which the mechanical properties of each constituent sheet were addressed separately. This is a new approach developed in the current work and it enables a fundamental correlation between multilayered material and its constituents. The mechanical properties of the bimetal clad sheet under the uniaxial tensions were predicted using those of the constituents and compared with calculations based on known rule of mixture (ROM) approach. The two approaches well reproduced stress–strain curves and r–value evolutions. The two FE modeling approaches were also applied to an independent test (i.e., the limiting dome height). The predictions were in good agreement with the experimental data. Finally, the mechanical properties of the bimetal clad sheet and constituents were correlated, and proper modeling scheme for multilayered materials is proposed.

Prediction method of thermal expansion coefficient for 2D composite fabric

In this article, an analytical method for predicting the thermal expansion coefficient of 2D composite fabrics is established. First of all, different from the traditional prediction of thermal expansion coefficient by classical laminate theory, this paper extends the self-consistent method of micromechanics to 2D composite fabrics to improve the accuracy of thickness direction prediction. Analytical Prediction method of Thermal expansion coefficient of 2D Composite fabric (APTC)was established. In the x section of Representative Volume Element(RVE) model, the stress balance equation and deformation coordination equation are applied to derive the internal thermal expansion coefficient. In the z section, the coefficient of thermal expansion is derived from the Poisson effect. Secondly, the formula of thermal expansion coefficient is derived for plain fabric, twill fabric and satin fabric respectively, which is consistent with the APTC method, indicating that the method is representative. Finally, RVE finite element model is established according to the braided structure parameters of 5284/T800 composite fabric. Comparing analytical solution, numerical solution and experimental solution, the α 1 and α 2 of the numerical and experimental solutions is close enough to be considered equal, which is consistent with the transverse isotropy of the composite fabric in xOy plane predicted by theoretical analysis.When predicting α 1 and α 2 , the maximum error between numerical solution and analytical solution is 1.79%, the maximum error between numerical solution and experimental solution is 22.29%, and the error remains at about 4% during predicting α 3 . In general, the APTC method for predicting the thermal expansion coefficient of composite fabrics is accurate and effective, and the simplified formula is more convenient to popularize and apply.

Multiscale formulation for materials composed by a saturated porous matrix and solid inclusions

Despite all the progress achieved in the characterization of heterogeneous materials by using multiscale paradigms based on the Representative Volume Element concept (RVE), there are still many aspects that demand ongoing development. We mention, for instance, in-homogeneous media with internal micro-structure comprising a mixture of components that require a dissimilar number/character of primary fields to describe their physical behavior. Micro-structures constituted by a saturated porous matrix (described through the pair displacement/pore pressure fields) endowed with impermeable non-porous solid inclusions (described only by the displacement field) are a typical example among many other practical applications. This new level of heterogeneity, between the set of primal descriptors for each micro-scale constituent, claims detailed revisions and novel adjustments in multiscale RVE-based theories.In this work, we present a first-order RVE-based formulation to address with the aforementioned phenomenology at a smaller length scale. At the macro-scale, the well-known poromechanics theory is preserved, where the constitutive response is provided by homogenization of the corresponding micro-scale problem. Two important attributes feature the model: (i) it is encompassed within the general framework posited by the “Method of Multiscale Virtual Power (MMVP)”, this leads to a variationally consistent methodology; (ii) our formulation preserves the objectivity with respect to the RVE-size (an indispensable but uncommon characteristic in RVE-based multiscale approaches for saturated porous media), due to it is based in a recent contribution of the authors (the so-called Selective Order Expansion technique “SOE”).The formulation is implemented following the squared Finite Element (FE2) numerical scheme. The potentialities of the model are demonstrated through a detailed series of numerical examples, including rigorous comparisons against the Direct Numerical Simulation (DNS) procedure.

Numerical Study of Concrete: A Mesoscopic Scale Simulation Methodology

This study aims to understand and simulate the mechanical properties of concrete, focusing specifically on the mesoscopic scale and its relation to the macro scale. Investigating concrete at this level involves examining its composition as a heterogeneous amalgamation of mortar, aggregates, and the Interfacial Transition Zone (ITZ). Numerical models, utilizing the finite element method (FEM), are employed to thoroughly examine the structural behavior of concrete. The study uses MATLAB (2023a) programming to develop three-dimensional models, which are then subjected to FEM analysis. Various mesoscopic Representative Volume Elements (RVEs) are formulated, considering spherical aggregates with different locations and dimensions to capture the complex nature of concrete. MATLAB is used to generate files containing comprehensive information about the RVEs, which are then processed with FEM to simulate compression strength tests. As the complexity increases with the inclusion of the ITZ, prismatic RVEs are developed to better represent real-world conditions. The proposed mesoscopic model establishes a foundational framework for a numerical simulation methodology tailored to laboratory compression tests, bridging the gap between mesoscopic and macroscopic scales. This approach provides detailed insights into concrete behavior, elucidating deformation and fracture mechanisms. Although not a complete substitute for experimental methods, these models offer a cost-effective and efficient alternative, identifying vulnerable areas and exploring the effects of additional materials on concrete behavior. The progressive replacement of laboratory tests with numerical simulations using RVEs of specific compositions will make the study of concrete behavior at the mesoscopic scale increasingly sustainable, paving the way for more efficient and environmentally friendly research practices in the field.

Electrical conductivity of multifunctional cementitious composites reinforced by graphene derivatives: A finite element-based computational micromechanics model

Graphene derivatives (GDs) are known for their considerable conductivity and electron mobility, making them promising candidates for developing electrically conductive cementitious composites (CCs). Consequently, CCs reinforced with GDs (CRGDs) can serve a variety of applications in the construction industry. Despite several experimental studies on the conductivity of CRGDs, finite element (FE) models have rarely been developed to investigate the conductivity of CRGDs. Previous FE models primarily focused on one-dimensional fillers like carbon nanotubes within polymeric matrices, overlooking the distinctive anisotropic behaviour of GDs in cementitious matrices. This study introduces a computational micromechanics model (CMM), integrating the representative elementary volume (REV) concept with FE analysis, to investigate the electrical conductivity of CRGDs. This model is particularly noted for its computational efficiency, as it converts the problem dimensions from 3D to 2D while ensuring an acceptable degree of accuracy. The CMM captures the anisotropic conductivity of GDs and includes critical conductivity mechanisms such as conductive pathways, the tunnelling effect, and field emission. Subsequently, REV size sensitivity analysis is performed to ensure the statistical relevance of the REV, followed by the model's validation against experimental data from the literature. Afterwards, the model's sensitivity analysis clarifies how different parameters, including the constituent properties, influence prediction results. The sensitivity analysis results suggest that the aspect ratio of GDs has considerable impacts on the electrical conductivity and the percolation threshold of CRGDs. Ultimately, this study aims to provide insights into the optimization and design of electrically conductive CRGDs and lay the foundation for future research in this area.

A finite strain micromechanical-based constitutive model: Application to porous polycrystal

A finite strain micromechanical-based constitutive model is developed for multi-phase polycrystal to characterize the interplay among strain hardening, texture evolution, and stress-strain relation. The phases are regarded as ellipsoidal inhomogeneities within the polycrystalline matrix, which can be considered as an aggregation of different orientated grains. At the grain level, the crystal plasticity model is applied to physically describe the inelastic deformation results from crystallographic slipping. At the polycrystalline level, an algorithmic finite strain self-consistent model is proposed to associate the strain and rotation rate at the grain level with the corresponding terms of the polycrystalline matrix rigorously. At the representative volume element level, the phases with different properties are investigated by extending the Mori–Tanaka model at finite strain. The algorithmic self-consistent model is validated by comparing the calculated normalized stress-strain curves and textures with those obtained by the model adopting extended Taylor hypothesis and the experimental data of stainless steel. Moreover, the grain and polycrystalline level behaviors calculated by the algorithmic self-consistent model are compared to those derived from the full-field model. The effects of void and elastic particles on overall mechanical behavior are analyzed by the extended Mori–Tanaka model, which is then verified by predicting the mechanical behavior of sintered nano-silver stub columns under stress-controlled compression loads.

A robust lattice Boltzmann scheme for high-throughput predicting effective thermal conductivity of reinforced composites

Accurately predicting effective thermal conductivity is of great importance for the design and performance evaluation of emerging composites. In this paper, an efficient and implementation-friendly lattice Boltzmann (LB) scheme for predicting the effective thermal conductivity of 3D complex structures is proposed. The key innovation is that the optimum convergence parameter of the 3D thermal LB method is found, which enables the LB equation to converge to steady heat conduction equation with the fastest speed and without losing any accuracy. To deal with the thermal contact resistance between different components, an interface treatment scheme is derived. In comparison with the existing schemes, the present scheme enjoys several hundred times higher computational efficiency. By virtue of this LB scheme, the effective thermal conductivity of the reinforced composites with different dimensional fillers are systematically calculated, and a comprehensive machine learning model is developed. This work provides a powerful numerical tool for high-throughput simulations of the 3D representative volume elements with high thermal conductivity ratios and large grid numbers. It may facilitate the application of data-driven techniques in study of the thermal transport properties of emerging composite materials and structures.