Browsing by Author "Sepasdar, Reza"

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    A Deep Learning Approach to Predict Full-Field Stress Distribution in Composite Materials
    Sepasdar, Reza (Virginia Tech, 2021-05-17)
    This thesis proposes a deep learning approach to predict stress at various stages of mechanical loading in 2-D representations of fiber-reinforced composites. More specifically, the full-field stress distribution at elastic and at an early stage of damage initiation is predicted based on the microstructural geometry. The required data set for the purposes of training and validation are generated via high-fidelity simulations of several randomly generated microstructural representations with complex geometries. Two deep learning approaches are employed and their performances are compared: fully convolutional generator and Pix2Pix translation. It is shown that both the utilized approaches can well predict the stress distributions at the designated loading stages with high accuracy.
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    Micromechanical Behavior of Fiber-Reinforced Composites using Finite Element Simulation and Deep Learning
    Sepasdar, Reza (Virginia Tech, 2021-10-07)
    This dissertation studies the micromechanical behavior of high-performance carbon fiber-reinforced polymer (CFRP) composites through high-fidelity numerical simulations. We investigated multiple transverse cracking of cross-ply CFRP laminates on the microstructure level through simulating large numerical models. Such an investigation demands an efficient numerical framework along with significant computational power. Hence, an efficient numerical framework was developed for simulating 2-D representations of CFRP composites' microstructure. The framework utilizes a nonlinear interface-enriched generalized finite element method (IGFEM) scheme which significantly decreases the computational cost. The framework was also designed to be fast and memory-efficient to enable simulating large numerical models. By utilizing the developed framework, the impacts of a few parameters on the evolution of transverse crack density in cross-ply CFRP laminates were studied. The considered parameters were characteristics of fiber/matrix cohesive interfaces, matrix stiffness, $0^{circ}$~plies longitudinal stiffness. We also developed a micromechanical framework for efficient and accurate simulation of damage propagation and failure in aligned discontinuous carbon fiber-reinforced composites under loading along the fibers' direction. The framework was validated based on the experimental results of a recently developed 3-D printed aligned discontinuous carbon fiber-reinforced composite as the composite of interest. The framework was then utilized to investigate the impacts of a few parameters of the constitutive equations on the strength and failure pattern of the composites of interest. This dissertation also contributes towards improving the computational efficiency of CFRP composites' simulations. We exhaustively investigated the cause of a convergence difficulty in finite element analyses caused by cohesive zone models (CZMs) which are commonly used to simulate fiber/matrix interfaces in CFRP composites. The CZMs' convergence difficulty significantly increases the computational burden. For the first time, we explained the root of the convergence difficulty and proposed a simple technique to overcome the convergence issue. The proposed technique outperformed the existing methods in terms of accuracy and computational cost. We also proposed a deep learning framework for predicting full-field distributions of mechanical responses in 2-D representations of CFRP composites based on the geometry of the microstructures. The deep learning framework can be used as a surrogate to the expensive and time-consuming finite element simulations. The proposed framework was able to accurately predict the stress distribution at an early stage of damage initiation and the failure pattern in representations of CFRP composites microstructure under transverse tension.