arXiv subjects

Behrouz Arash

Publications and source records attributed to Behrouz Arash.

13 recordsLinked to original sources

arXiv2026

Bridging nanoparticle morphology and viscoelastic behavior in epoxy nanocomposites: A coarse-grained simulation-informed constitutive model

Accurate prediction of the material behavior of polymer nanocomposites under various thermomechanical loading conditions is increasingly demanded for engineering applications. This study proposes an integrated framework combining coarse-grained (CG) molecular simulations and experimental testing to develop predictive constitutive models for nanoparticle/epoxy nanocomposites. The key contribution of this work lies in characterizing the influence of nanoparticle content and agglomerate size on the rate- and temperature-dependent behavior of nanocomposites, enabled by large-scale CG simulations. The proposed framework successfully captures the material response, including nonlinear hyperelasticity, softening behavior, and rate- and temperature-dependent properties, across a broad range of strain rates, temperatures, and nanoparticle sizes and weight fractions. The predictive capability of the CG simulation-informed constitutive model is validated using additional experimental data that were not included in the parameter identification process. By reducing reliance on extensive experimental testing while maintaining high accuracy, this simulation-driven approach offers an efficient pathway for developing robust, predictive constitutive models for designing and optimizing advanced nanocomposites.

cs.CE↗

arXiv2026

Data-Driven Structural Health Monitoring of Short Carbon Fiber-Reinforced Polymer Composites via Multiphysics Phase-Field Simulation

Short carbon fiber-reinforced polymer (SCFRP) composites exploit the intrinsic conductivity of the carbon fiber network for self-sensing, yet no predictive model couples their anisotropic, rate-dependent fracture to piezoresistive damage identification. This work presents a finite deformation multiphysics phase-field framework coupling a viscoelastic-viscoplastic constitutive model, an anisotropic crack resistance formulation, and a piezoresistive conductivity model. The three sub-problems are unified through the second-order fiber orientation tensor, which simultaneously defines fiber family directions, crack resistance anisotropy, and principal conduction paths of the carbon fiber network. A damage-coupled conductivity tensor captures both strain-driven geometric-kinematic resistance changes and irreversible network severance driven by the phase-field variable. The framework is coupled to an eight-electrode electrical impedance tomography configuration, and the normalized inter-electrode conductance ratios serve as inputs to a feedforward artificial neural network that infers normalized crack length and mechanical compliance without mechanical sensing. The network achieves R2 = 0.99 on held-out configurations, confirming generalization across the microstructure space. The framework establishes a physics-based, computationally efficient route for real-time structural health monitoring and inverse damage assessment in SCFRP composites.

cs.CE↗

arXiv2026

A phase-field framework for anisotropic viscoelastic-viscoplastic fracture in short fiber-reinforced polymers in hygrothermal environments

This work presents a comprehensive phase-field framework for modeling anisotropic viscoelastic-viscoplastic fracture in short fiber-reinforced polymer (SFRP) composites under hygrothermal environments at finite deformation. The constitutive model employs a multiplicative decomposition of the deformation gradient into viscoelastic and viscoplastic components. An anisotropic phase-field formulation is developed using structural tensors to capture orientation-dependent fracture energy induced by multiple fiber families. Hygrothermal effects are incorporated through moisture-dependent swelling, thermal expansion, and temperature- and moisture-sensitive material parameters within the coupled framework. Numerical investigations demonstrate the framework's capability to capture complex fracture phenomena in SFRPs. Results reveal that fiber orientation fundamentally governs the spatial distribution of crack driving force, with maximum energy accumulation along fiber directions persisting throughout viscous relaxation. The anisotropy parameter controlling directional fracture resistance significantly influences crack path deflection. Hygrothermal degradation substantially reduces both peak load and fracture energy, with moisture absorption and elevated temperature each contributing to decreased mechanical performance. The framework captures the influence of fiber mechanical properties on global load-bearing capacity and crack propagation resistance. This unified computational framework advances the predictive modeling of damage evolution in SFRPs subjected to realistic environmental and mechanical loading conditions.

cs.CE↗

arXiv2025

A hybrid electromechanical phase-field and deep learning framework for predicting fracture in dielectric nanocomposites

The accurate and efficient prediction of crack propagation in dielectric materials is a critical challenge in structural health monitoring and the design of smart systems. This work presents a hybrid modeling framework that combines an electromechanical phase-field fracture model with deep learning-based surrogate modeling to predict fracture evolution in dielectric nanocomposite plates. The underlying finite element simulations capture the coupling between mechanical deformation and electrical field perturbations caused by cracks, using a variational phase-field formulation. High-fidelity simulation outputs - namely, phase-field damage variables and electric potential fields -- are used to train convolutional neural networks (CNNs) with ResNet-U-Net architectures for pixel-wise segmentation of crack paths. The study systematically compares the performance of CNNs trained on phase-field versus electric potential data across multiple ResNet backbones. The results reveal that electric potential fields, although they encode damage indirectly, offer superior segmentation accuracy, faster convergence, and enhanced generalization, owing to their smoother gradient distribution and global spatial coverage. The proposed framework significantly reduces computational costs while preserving high accuracy, offers potential when appropriately adapted for sensor-based input data.

physics.comp-ph↗

arXiv2025

Optimizing the Charging of Open Quantum Batteries using Long Short-Term Memory-Driven Reinforcement Learning

Controlling the charging process of a quantum battery involves strategies to efficiently transfer, store, and retain energy, while mitigating decoherence, energy dissipation, and inefficiencies caused by surrounding interactions. We develop a model to study the charging process of a quantum battery in an open quantum setting, where the battery interacts with a charger and a structured reservoir. To overcome the limitations of static charging protocols, a reinforcement learning (RL) charging strategy is proposed, which utilizes the deep deterministic policy gradient algorithm alongside long short-term memory (LSTM) networks. The LSTM networks enable the RL model to capture temporal correlations driven by non-Markovian dynamics, facilitating a continuous, adaptive charging strategy. The RL protocols consistently outperform conventional fixed heuristic strategies by real-time controlling the driving field amplitude and coupling parameters. By penalizing battery-to-charger backflow in the reward function, the RL-optimized charging strategy promotes efficient unidirectional energy transfer from charger to battery, achieving higher and more stable extractable work. The proposed RL controller would provide a framework for designing efficient charging schemes in broader configurations and multi-cell quantum batteries.

quant-ph↗

arXiv2024

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.

cs.LG↗

arXiv2024

Cyclic viscoelastic-viscoplastic behavior of epoxy nanocomposites under hygrothermal conditions: A phase-field fracture model

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 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, with distinct definitions for tension and compression. The proposed derivation offers a realistic modeling of damage and viscoplasticity mechanisms in the nanocomposites by coupling the phase-field damage model with a modified crack driving force 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 single-edge notched nanocomposite plates subjected to monolithic tensile and shear loading.

cs.CE↗

arXiv2023

A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content

In this work, we propose a deep learning (DL)-based constitutive model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites with moisture content. For this, a long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress-strain relationship and consistent tangent moduli. In addition, the DL-based constitutive model is implemented into finite element analysis. Finite element simulations are performed to study the effect of load rate and moisture content on the force-displacement response of nanoparticle/ epoxy samples. Numerical examples show that the computational efficiency of the DL model depends on the loading condition and is significantly higher than the conventional constitutive model. Furthermore, comparing numerical results and experimental data demonstrates good agreement with different nanoparticle and moisture contents.

cs.LG↗

arXiv2018

Modal analysis for determining the size-and temperature-dependent bending rigidity of graphene

The bending rigidity of two-dimensional (2D) materials is a key parameter for understanding the mechanics of 2D NEMS devices. The apparent bending rigidity of graphene membranes at macroscopic scale differs from theoretical predictions at micro-scale. This difference is believed to originate from thermally induced dynamic ripples in the atomically thin membrane. In this paper, we perform modal analysis to estimate the effective macroscopic bending rigidity of graphene membranes from the frequency spectrum of their Brownian motion. Our method is based on fitting the resonance frequencies obtained from the Brownian motion in molecular dynamics simulations, to those obtained from a continuum mechanics model, with bending rigidity and pretension as the fit parameters. In this way, the effective bending rigidity of the membrane and its temperature and size dependence, are extracted, while including the effects of dynamic ripples and thermal fluctuations. The proposed method provides a framework for estimating the macroscopic mechanical properties at high frequencies in other two-dimensional nano-structures at finite temperatures.

physics.app-ph↗

arXiv2017

Mechanical properties of carbon nanotube reinforced polymer nanocomposites: A coarse-grained model

In this work, a coarse-grained (CG) model of carbon nanotube (CNT) reinforced polymer matrix composites is developed. A distinguishing feature of the CG model is the ability to capture interactions between polymer chains and nanotubes. The CG potentials for nanotubes and polymer chains are calibrated using the strain energy conservation between CG models and full atomistic systems. The applicability and efficiency of the CG model in predicting the elastic properties of CNT/polymer composites are evaluated through verification processes with molecular simulations. The simulation results reveal that the CG model is able to estimate the mechanical properties of the nanocomposites with high accuracy and low computational cost. The effect of the volume fraction of CNT reinforcements on the Young's modulus of the nanocomposites is investigated. The application of the method in the modeling of large unit cells with randomly distributed CNT reinforcements is examined. The established CG model will enable the simulation of reinforced polymer matrix composites across a wide range of length scales from nano to mesoscale.

physics.app-ph↗

arXiv2017

Tensile fracture behavior of short carbon nanotube reinforced polymer composites: A coarse-grained model

Short-fiber-reinforced polymer composites are increasingly used in engineering applications and industrial products owing to their unique combination of superior mechanical properties, and relatively easy and low cost manufacturing process. The mechanical behavior of short carbon nanotube (CNT) polymer composites, however, remains poorly understood due to size and time limitations of experiments and atomistic simulations. To address this issue, the tensile fracture behavior of short CNT reinforced poly (methyl methacrylate) (PMMA) matrix composites is investigated using a coarse-grained (CG) model. The reliability of the CG model is demonstrated by reproducing experimental results on the stress-stain behavior of the polymer material. The effect of the nanotube weight fraction on the mechanical properties, i.e. the Young's modulus, yield strength,tensile strength and critical strain, of the CNT/polymer composites is studied in detail. The dependence of the mechanical properties of the composites on the orientation and length-to-diameter aspect ratio of nanotube reinforcements is also examined.

physics.app-ph↗

arXiv2017

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.

physics.comp-ph↗

arXiv2017

A coarse-grained model for the elastic properties of cross linked short carbon nanotube/polymer composites

Short fiber reinforced polymer composites have found extensive industrial and engineering applications owing to their unique combination of low cost, relatively easy processing and superior mechanical properties compared to their parent polymers. In this study, a coarse grained (CG) model of cross linked carbon nanotube (CNT) reinforced polymer matrix composites is developed. A characteristic feature of the CG model is the ability to capture the interactions between polymer chains, and nanotubes and polymer matrix. The dependence of the elastic properties of the composites on the mole fraction of cross links, and the weight fraction and distribution of nano tube reinforcements is discussed. The simulation results reveal that the fictionalization of CNTs using methylene cross links is a key factor toward signicantly increasing the elastic properties of randomly distributed short CNT reinforced poly (methyl methacrylate) (PMMA) matrix. The applicability of the CG model in predicting the elastic properties of CNT reinforced polymer composites is also evaluated through a verification process with a micromechanical model for unidirectional short fibers.

physics.comp-ph↗