Coarse-grained model of the J-integral of carbon nanotube reinforced polymer composites

The J-integral is recognized as a fundamental parameter in fracture mechanics that characterizes the inherent resistance of materials to crack growth. However, the conventional methods to calculate the J-integral, which require knowledge of the exact position of a crack tip and the continuum fields around it, are unable to precisely measure the J-integral of polymer composites at the nanoscale. This work aims to propose an effective calculation method based on coarse-grained (CG) simulations for predicting the J-integral of carbon nanotube (CNT)/polymer composites. In the proposed approach, the J-integral is determined from the load displacement curve of a single specimen. The distinguishing feature of the method is the calculation of J-integral without need of information about the crack tip, which makes it applicable to complex polymer systems. The effects of the CNT weight fraction and covalent cross-links between the polymer matrix and nanotubes, and polymer chains on the fracture behavior of the composites are studied in detail. The dependence of the J-integral on the crack length and the size of representative volume element (RVE) is also explored.Comment: arXiv admin note: text overlap with arXiv:1704.0145

Announcement of a virtual special issue on computational carbon nanoscience

The Carbon journal is pleased to introduce a themed collection of recent articles in the area of computational carbon nanoscience. This virtual special issue was assembled from previously published Carbon articles by Guest Editors Quan Wang and Behrouz Arash, and can be accessed as a set in the special issue section of the journal website homepage: www.journals.elsevier.com/carbon. The article below by our guest editors serves as an introduction to this virtual special issue, and also a commentary on the growing role of computation as a tool to understand the synthesis and properties of carbon nanoforms and their behavior in composite materials

Effect of moisture on the nonlinear viscoelastic fracture behavior of polymer nanocompsites: a finite deformation phase-field model

The mechanisms underlying damage in high-performance polymer nanocomposites are remarkably affected by hygrothermal conditions. In this study, we develop a phase-field formulation to investigate the influence of hygrothermal conditions on the nonlinear viscoelastic fracture behavior of epoxy resins and their nanocomposites at finite deformation. For this, the Helmholtz free energy, capturing the effect of temperature and moisture and nanoparticle contents, is defined based on an additive decomposition of the energy into an equilibrium, a non-equilibrium, and a volumetric contribution with different definitions under tensile and compressive loading. The coupled displacement phase-field problem is solved using a quasi-Newton monolithic algorithm and a staggered solution scheme. Numerical examples show that the monolithic algorithm is more efficient. Simulations are performed to investigate the effect of temperature, deformation rate, and moisture content on the force–displacement response of boehmite nanoparticle/epoxy samples in benchmark numerical problems. Comparing numerical predictions and experimental data for compact-tension tests shows good agreement at different nanoparticle contents. Also, the model’s capability to predict fracture patterns is evaluated using simulations of single-edge notched nanocomposite plates under tensile and shear loading. © 2022, The Author(s)

Announcement of a virtual special issue on computational carbon nanoscience

The Carbon journal is pleased to introduce a themed collection of recent articles in the area of computational carbon nanoscience. This virtual special issue was assembled from previously published Carbon articles by Guest Editors Quan Wang and Behrouz Arash, and can be accessed as a set in the special issue section of the journal website homepage: www.journals.elsevier.com/carbon. The article below by our guest editors serves as an introduction to this virtual special issue, and also a commentary on the growing role of computation as a tool to understand the synthesis and properties of carbon nanoforms and their behavior in composite materials

Phase-field modeling of fracture in viscoelastic–viscoplastic thermoset nanocomposites under cyclic and monolithic loading

In this study, a finite deformation phase-field formulation is developed to investigate the effect of hygrothermal conditions on the viscoelastic–viscoplastic fracture behavior of epoxy nanocomposites under cyclic and monolithic loading. The formulation incorporates a definition of the Helmholtz free energy, which considers the effect of nanoparticles, moisture content, and temperature. The free energy is additively decomposed into a deviatoric equilibrium, a deviatoric non-equilibrium, and a volumetric contribution. The proposed derivation offers a realistic modeling of damage and viscoplasticity mechanisms in the nanocomposites by coupling the phase-field damage model and a viscoelastic–viscoplastic model. Numerical simulations are conducted to study the cyclic force–displacement response of both dry and saturated boehmite nanoparticle (BNP)/epoxy samples, considering BNP contents and temperature. Comparing numerical results with experimental data shows good agreement at various BNP contents. In addition, the predictive capability of the phase-field model is evaluated through simulations of notched nanocomposite plates subjected to monolithic tensile and shear loading

A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites

This work proposes a physics-informed deep learning (PIDL)-based constitutive model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticle-filled epoxies under various ambient conditions. The deep-learning model is trained to enforce thermodynamic principles, leading to a thermodynamically consistent constitutive model. To accomplish this, a long short-term memory network is combined with a feed-forward neural network to predict internal variables required for characterizing the internal dissipation of the nanocomposite materials. In addition, another feed-forward neural network is used to indicate the free-energy function, which enables defining the thermodynamic state of the entire system. The PIDL model is initially developed for the three-dimensional case by generating synthetic data from a classical constitutive model. The model is then trained by extracting the data directly from cyclic loading-unloading experimental tests. Numerical examples show that the PIDL model can accurately predict the mechanical behavior of epoxy-based nanocomposites for different volume fractions of fibers and nanoparticles under various hygrothermal conditions.Comment: arXiv admin note: text overlap with arXiv:2305.0810