Abstract
This study explores the dynamic deflection and vibrational analysis of a spherical shell, modeled as a football game ball, reinforced with graphene platelet nanocomposites (GPLs). The analysis leverages the Carrera Unified Formulation (CUF) for accurate and efficient modeling of the mechanical behavior of the shell under dynamic loads. CUF’s flexibility in adapting to complex geometries and material properties is utilized to represent the heterogeneous reinforcement of GPLs within the spherical shell structure. To enhance the reliability of the computational results, a hybrid artificial intelligence (AI) framework is implemented for result verification. This framework integrates Convolutional Neural Networks (CNNs) for spatial data representation with ReliefF feature selection to identify and prioritize influential variables. The hybrid AI system ensures robust predictive modeling, addressing the high-dimensional nature of the problem domain. The study also delves into the implications of graphene reinforcement on the ball’s performance, focusing on factors such as deformation under load, vibrational response, and stability thresholds. The results indicate that GPL reinforcement significantly improves the dynamic stability and stiffness of the spherical shell. Comparative analyses validate the efficiency of the CUF-based computational approach through mathematics benchmarks and AI-verified predictions. This interdisciplinary work highlights the potential of combining advanced computational mechanics, nanomaterials, and AI-driven verification in optimizing dynamic stability for applications in sports engineering and beyond.
Published Version
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