This study investigates the effects of incorporating various types of nanoparticles, both singularly and in hybrid form, on the low-velocity impact (LVI) response of glass fiber reinforced polymer (GFRP) composites. GFRP composites were fabricated using the hand lay-up method and different weight percentages (wt. %) of multi-walled carbon nanotubes (MWCNT), clay, TiO₂, and CuO nanoparticles were added into the matrix of composites. To test the LVI response, 14 types of specimens were fabricated with single and hybrid nanoparticle loadings, and LVI tests were conducted using 5 and 10-cm span dimensions at two levels of subjected energy. The experimental results reveal that specimens with a single loading of MWCNT or nano-clay have a lower maximum contact force compared to pure specimens with fully rebounding behavior. This indicates that neither 5 nor 10 cm spans result in severe damages during the impact tests. Furthermore, incorporating more MWCNTs results in stiffer behavior and more brittleness. The study also explores the synergetic effect of adding hybrid nanoparticles in the fabricated composites and discusses the calculated results for absorbed energy. Finally, scanning electron microscopy (SEM) images are analyzed to evaluate the enhancement mechanisms resulting from the addition of nanoparticles to GFRP composite specimens.

The simulation of damage in composite materials is an important research area that impacts different engineering applications from aerospace structures to renewable energy systems. This review provides a comprehensive analysis of current damage modeling approaches, including intra-layer and inter-layer failures. Various numerical strategies, such as continuum damage mechanics (CDM), cohesive zone models (CZM), extended finite element methods (XFEM), phase-field models (PFM), and peridynamics (PD), are examined to assess their efficiency in predicting crack initiation, propagation, and interaction. Additionally, the role of data-assisted (driven) techniques, such as machine learning, in enhancing predictive capabilities is explored. This review highlights the strengths and limitations of each approach, underscoring the need for further advancements in computational efficiency, multiscale modeling, and integration with experimental data. The findings serve as a foundation for future research into optimizing damage prediction techniques to improve the reliability and durability of composite structures.