arXiv subjects

Shadab Zakavati

Publications and source records attributed to Shadab Zakavati.

4 recordsLinked to original sources

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

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

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↗