A Comprehensive Numerical Study of the Effect of Hybrid Reinforcement of Fiber Sizing on the Transverse Elastic Modulus of Polymeric Nanocomposites
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Abstract
Abstract One of the most critical degradation modes in polymeric composites is fiber–matrix debonding. Therefore, utilizing nanoparticles in the fiber sizing instead of dispersing nanoparticles in the matrix as a traditional method could postpone the separation of fibers from the matrix. Covering of fibers during the production process is called sizing. The present study simulates two three-dimensional representative volume elements (RVEs) to predict the transverse elastic modulus of the glass/epoxy composite. The sizing region in the RVEs, provided in Abaqus software, is simulated with both homogeneous and heterogeneous mechanical properties. Then the numerical models are validated using the available numerical and experimental data. Furthermore, the Mori–Tanaka, Halpin–Tsai, and random distribution methods are employed to calculate equivalent properties for the nanoparticle-reinforced sizing, which are used for the sizing region of the RVEs to predict the transverse elastic modulus of the four-phase glass/epoxy composite. Compared to the available experimental data, the random distribution method is a more accurate procedure to predict the transverse Young’s modulus. Finally, with the assistance of the random distribution method, nanoparticles with different dimensions or even types are dispersed in the sizing region. In fact, carbon nanofibers (CNFs) and silica (SiO2) nanoparticles are simultaneously distributed in the sizing with various dimensions to predict the overall transverse elastic modulus of the composite. Once again, these nanoparticles are modeled in the sizing region with specific measurements. Besides, the results for all of the states are compared.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Carbon Nanotubes in Composites · Polymer Nanocomposites and Properties · Composite Material Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (28)
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