Numerical Analysis of Load Transfer Mechanism in Fiber-Reinforced Composites Enhanced by Zinc Oxide Nanowires

In this study, a three-dimensional model of single carbon-fiber composites enhanced by radially grown zinc oxide (ZnO) nanowires is investigated numerically. Due to the different length scales of the composites and the theories used in the system, a multi-scale analysis is employed to simulate the behavior of the fiber-reinforced composites. The effective mechanical properties of the enhancement layer are extracted at the micro-scale by the homogenization analysis of an appropriate representative volume element. The fiber interface is modeled at the meso-scale utilizing the cohesive zone method. A thin layer of interface with the cohesive element is modeled around the fiber. The material properties of the interface are evaluated based on the properties of fiber and the enhancement layer. The macro-scale damage behavior of fiber is defined by user-defined mechanical material behavior. Single fiber fragmentation test is simulated in ABAQUS by applying the tensile loads on the structure. The load transfer mechanism is evaluated by capturing the number of fiber fragmentation and calculating the interfacial shear strength. The effect of different ZnO diameters and volume fractions are also investigated. The results show stronger interface and higher load transfer capacity in the enhanced composite compared to the bare composite.

Deep-learning-based macro-pixel synthesis and lossless coding of light field images

This paper proposes a novel approach for lossless coding of light field (LF) images based on a macro-pixel (MP) synthesis technique which synthesizes the entire LF image in one step. The reference views used in the synthesis process are selected based on four different view configurations and define the reference LF image. This image is stored as an array of reference MPs which collect one pixel from each reference view, being losslessly encoded as a base layer. A first contribution focuses on a novel network design for view synthesis which synthesizes the entire LF image as an array of synthesized MPs. A second contribution proposes a network model for coding which computes the MP prediction used for lossless encoding of the remaining views as an enhancement layer. Synthesis results show an average distortion of 29.82 dB based on four reference views and up to 36.19 dB based on 25 reference views. Compression results show an average improvement of 29.9% over the traditional lossless image codecs and 9.1% over the state-of-the-art.