Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks(Pergamon, 2020-08-15) Dewapriya, M. A. N; Rajapakse, R. K. N. D; Dias, W. P. SAdvanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction. A part of the data required to model the shallow networks was obtained by developing an analytical solution based on the Bailey durability criterion and the Arrhenius equation. Molecular dynamics (MD) simulations were also used to obtain some data. Sensitivity analysis was performed to explore the features learnt by the neural network, and their behaviour under extrapolation was also investigated. Subsequently, deep convolutional neural networks (CNNs) were developed to predict the fracture stress of graphene samples containing random distributions of vacancy defects. Data required to model CNNs was obtained from MD simulations. Our results reveal that the neural networks have a strong ability to predict the fracture stress of defective graphene under various processing conditions. In addition, this work highlights some advantages as well as limitations and challenges in using neural networks to solve complex problems in the domain of computational materials design.Publication Embargo Torsional buckling of carbon nanotubes based on nonlocal elasticity shell models(Elsevier, 2010-06-01) Khademolhosseini, F; Rajapakse, R. K. N. D; Nojeh, AThis paper investigates size-effects in the torsional response of single walled carbon nanotubes (SWCNTs) by developing a modified nonlocal continuum shell model. The purpose is to facilitate the design of devices based on SWCNT torsion by providing a simple, accurate and efficient continuum model that can predict the corresponding buckling loads. To this end, Eringen’s equations of nonlocal elasticity are incorporated into the classical models for torsion of cylindrical shells given by Timoshenko and Donnell. In contrast to the classical models, the nonlocal model developed here predicts non-dimensional buckling torques that depend on the values of certain geometric parameters of the CNT, allowing for the inclusion of size-effects. Molecular dynamics simulations of torsional buckling are also performed and the results of which are compared with the classical and nonlocal models and used to extract consistent values of shell thickness and the nonlocal elasticity constant (e0). A thickness of 0.85 Å and nonlocal constant values of approximately 0.8 and 0.6 for armchair and zigzag nanotubes respectively are recommended for torsional analysis of SWCNTs using nonlocal shell models. The size-dependent nonlocal models together with molecular dynamics simulations show that classical shell models overestimate the critical buckling torque of SWCNTs and are not suitable for modeling of SWCNTs with diameters smaller than 1.5 nm.