Testing and multiscale modeling of a foam-based lung surrogate subjected to underwater shock loading

Multiscale modeling: Prediction for thermophysical properties of needled carbon/carbon composite and evaluation of brake disk system

Crystal plasticity finite element (CPFEM) modeling of metals that can be age hardened consisting of second phase particles is extensively performed based on representative volume element (RVE) models. The RVE model is generated for ferritic low-carbon steel using the data obtained from microstructural observation through optical microscopy (OM) and electron backscatter diffraction (EBSD). The generated RVE is required to statistically represent the original material in terms of grain topology and texture in microscale, as well as the configuration of second phase particles in submicron scale. The multiscale, multi-phase nature of the generated RVE leads to a computationally expensive modeling procedure. To overcome this issue, an alternative multiscale modeling approach based on a homogenization scheme is proposed, in which the effect of second phase particles on deformation behavior is accounted for with no need for the explicit presence of particles in RVE. Lastly, a thorough parametric analysis is performed to investigate the sensitivity of the mechanical properties to the second phase particles in terms of size, volume fraction, geometrical distribution, and deformable or non-deformable properties of precipitates in the investigated material. The results show that the proposed multiscale modeling approach successfully accounts for the effect of second phase particles on deformation behavior, while the computational cost is reduced by more than 99%. In addition, the simulations show that the configuration of second phase particles at a microscale plays an important role in defining the mechanical behavior of the material.

In this study, a numerical representative volume element (RVE) model was used to predict the mechanical properties of a Rice Husk Particulate (RHP)-Epoxy composite for use as an alternative material in non-critical applications. Seven different analytical models Counto, Ishai-Cohen, Halpin-Tsai, Nielsen, Nicolais, Modified Nicolais and Pukanszky were used as comparison tools for the numerical model. The mechanical properties estimated for 0%, 10% and 30% RHP-Epoxy composites using the numerical and analytical models are in general agreement with each other. Using the analytical models, it was calculated that an increase in volume percentage of RHP to 30% led to continual reduction in elastic Young’s modulus and ultimate tensile strength of the composite. The numerical RVE models also predicted a similar trend between filler volume percentage and material properties. Overall, the results of this study suggest that RHP can be used to reduce the composite raw material costs by replacing the more expensive polymer content with agricultural waste products with limited compromise to the composite’s mechanical properties.

Evolution of elliptic-cylindrical and circular-cylindrical voids inside power-law viscous solids

An integrated computational materials engineering framework to analyze the failure behaviors of carbon fiber reinforced polymer composites for lightweight vehicle applications

Purpose The purpose of this study to analyze the plastic anisotropy of 6061 aluminum alloy with finite deformation using crystal plasticity finite element method. Design/methodology/approach A representative volume element (RVE) model was constructed by Voronoi tessellation. In this model, grain shapes, grain orientations and distribution of grains were involved. The mechanical response of the component under composite loading was tested using specify cruciform specimen. Moreover, different stress and strain states in the specific central region were analyzed to reveal the effects of complex loading. Findings We calculated the influence of misorientation of adjacent grains as well as the evolution of the micro structure’s plastic deformation on the macroscopic deformation of the structure. Geometry design for the cruciform specimen helps obtain a homogenous distribution of the stress and strain at the specimen center. In this process, the initial grain orientation is also an important factor, and the larger misorientations between special grains may cause greater stress concentration. Originality/value The influence of micro-scale factors on macro-scale plastic anisotropy of AA6061 is analyzed using RVE model and cruciform specimen, and they offer a reference for related research.

The present article is focused on the in-house synthesis of graphene nano-flakes (size range: 3–8 nm) reinforced Al-alloy (AA6005 series) nanocomposites using stir casting process. The microstructure of the obtained Al nanocomposites at different concentration of flake shaped graphene nanoparticles (GRNPs) show that the particles at 2 wt% and 4 wt% found to be distributed extensively on the surfaces of Al alloy matrix but observed negligible across the grain boundary whereas in the case of 6 wt% concentrated developed composite specimen, the GRNPs were observed to be well dispersed both on the surfaces and grain boundary of the matrix. With the addition of the particles, there found to be the formation of more voids in the nanocomposite specimens. The experimental characterization results reveal that with the increase in graphene content to 6 wt%, the tensile strength, compressive strength, impact energy, hardness and wear resistance of the nanocomposites were increased by 9% to 36%, 30% to 44%, 9.8 J, 36.03 HRB and 33% respectively as compared to unreinforced alloy. It was observed that the composites with increased concentration of reinforcement exhibits brittle behaviour and at 6 wt% GRNPs, the elongation is almost found to be 50% lower than the unreinforced one. Further, a 3D microstructure representative volume element (RVE) model of aluminium nanocomposite is generated using Digimat-FE software. Then, microstructural deformation behaviour of the nanocomposite is realized by RVE model. The simulation results reveal that the tensile property of the aluminium nanocomposites predicted using RVE model is in well agreement with the experimental values.

In this work, three-dimensional (3D) finite element simulations were undertaken to study the effects of lightning strikes on the microscale behaviour of continuous fibre-reinforced composite materials and to predict and understand complex lightning damage mechanisms. This approach is different from the conventional mesoscale or macroscale level of analysis, that predicts the overall lightning damage in composite laminates, thus providing better understanding of lightning-induced thermo-mechanical damage at a fundamental level. Micromechanical representative volume element (RVE) models of a UD composite laminate were created with circular carbon fibres randomly distributed in an epoxy matrix. The effects of various grounding conditions (one-, two-, and four-side grounding), fibre volume fractions ( V f = 55, 60%, and 65%), and peak current amplitudes (10, 20, and 40 kA) on microscopic damage in RVE models were characterised to understand fibre, epoxy matrix, and fibre-matrix interfacial damage associated with a lightning strike. Thermal damage, estimated based on epoxy matrix decomposition temperature, was largely constant up to 20 kA before increasing significantly at 40 kA. Severe thermal damage steadily decreased with increasing V f. Thermo-mechanical damage was predicted using a ductile plasticity model with Drucker–Prager yield criterion for epoxy matrix failure, and cohesive surfaces for fibre-matrix interface debonding. Thermal strain had the largest contribution to thermo-mechanical damage, while dynamic pressure loading was negligible in all RVE models. The RVE model proposed in this work, to the best of the authors’ knowledge, is the first model predicting lightning-induced fibre-matrix interfacial damage.

Heterogenous microstructures of High-Pressure Die-Casting (HPDC) Al–Si alloy lead to random strain concentrations during deformation. Different microstructural variations are considered, including Fe-rich phase and porosity, with focuses on different geometrical aspects. Representative Volume Element (RVE) models were established based on Electron Back Scatter Diffraction (EBSD) data using Finite Element Method (FEM). The effectiveness of the RVE models was verified by comparing predicted strain concentration locations with experimental results. The microstructural models were refined to explore effects of microstructural changes. The simulation results show that there is an interactive effect of porosity and Fe-rich phase on the microscopic strain field. When there is no porosity, the geometric properties of Fe-rich phase play an important role in changes of microscopic strain field. When average diameter of porosity is close to that of Fe-rich phase, position and shape of porosity dominate the microscopic strain field compared with that of Fe-rich phase.

Modeling of frontal polymerization of carbon fiber and dicyclopentadiene woven composites with stochastic material uncertainty

Multiscale modeling for the impact behavior of 3D angle-interlock woven composites

This paper presents a representative volume element (RVE)-based modelling method to capture the mechanical behaviour of aluminum foams under compressive loadings. Octadecahedron is selected as a geometric basis shape to form closed cells of the aluminum foams in the microstructured RVE model to simulate the mechanical behaviour under compressive loadings. The stress-strain relationship obtained from the numerical modelling is compared to that from experimental study and agreements between these results demonstrate the validity of the proposed RVE model. Through observing the deformation evolution of cells during a compressive loading process, the failure modes of aluminum foams are identified and analysed using the proposed RVE model. Further the influence of strain rate on the mechanical behaviour of aluminum foams under compressive loadings is numerically studied via a parametric study.

A hierarchical multiscale off-road mobility model is enhanced through the development of an artificial neural network (ANN) surrogate model that captures the complex material behavior of deformable terrain. By exploiting the learning capability of neural networks, the incremental stress and strain relationship of granular terrain is predicted by the ANN representative volume elements (RVE) at various states of the stress and strain. A systematic training procedure for ANN RVEs is developed with a virtual tire test rig model for producing training data from the discrete-element (DE) RVEs without relying on computationally intensive full vehicle simulations on deformable terrain. The ANN surrogate RVEs are then integrated into the hierarchical multiscale computational framework as a lower-scale model with the scalable parallel computing capability, while the macroscale terrain deformation is described by the finite element (FE) approach. It is demonstrated with several numerical examples that off-road vehicle mobility performances predicted by the proposed FE-ANN multiscale terrain model are in good agreement with those of the FE-DE multiscale model while achieving a substantial computational time reduction. The accuracy and robustness of the ANN RVE for fine-grain sand terrain are discussed for scenarios not considered in training datasets. Furthermore, a drawbar pull test simulation is presented with the ANN RVE developed with data in the cornering scenario and validated against the full-scale vehicle test data. The numerical results confirm the predictive ability of the FE-ANN multiscale terrain model for off-road mobility simulations.

Vie des enterprises

Improving the ductile deformation behavior of Mg-SiC nanocomposites without compromising strength is critical to enhancing their mechanical properties. Mg-SiC nanocomposites are produced through mechanical milling, cold isostatic pressing, sintering, and hot extrusion processes. This study investigates the uniaxial stress–strain response and deformation behavior of the Mg-SiC nanocomposite compared to pure Mg samples with and without the milling process. The deformation behavior was investigated by two-dimensional (2D) digital image correlation (DIC) at two macroscopic and microscopic scales, employing light micrographs and in situ loading samples, respectively, in the scanning electron microscope. Compared to the pure Mg samples, the mechanical test results demonstrated a significant improvement in strength (80 MPa) and fracture strain (23.5%) of the Mg-SiC nanocomposite. The three-dimensional (3D) representative volume element (RVE) model revealed the particle dispersion effect on the mechanical properties of the nanocomposite. The RVE results demonstrate ductile deformation behavior in the sample with homogenous dispersion of SiC particles compared with the heterogeneous dispersion of SiC particles in Mg-SiC nanocomposite. The results demonstrated a good agreement between DIC and RVE predictions for Mg-SiC nanocomposites across macro- and microscales.