Research on applicable sensor for solving the volleyball sport problem using smart nanomaterial based on dynamic simulation

This research investigates the application of machine learning methodologies to optimize energy management within the context of a volleyball game, specifically focusing on the energy dynamics of the ball. Machine learning, as a discipline, provides a robust framework for the development of automated analytical models, enabling the extraction of meaningful insights from complex datasets. The ball in the volleyball games is the most important tool. The surface properties and response to hit by hand are crucial in determining the accuracy and fluency of the game. The outer material of the ball is extremely determinative in the mechanical response of the ball to the impact loading which commonly causes vibration in the ball. Therefore, in the current work vibrations of a volleyball game ball is presented. The volleyball game ball is reinforced by graphene oxide powders to improve its stability in different situation. Finally, the results show that the ball's radius has a key role in the dynamic stability of the volleyball game ball. One of the important outcomes of the current research is that, unlike the ball's size, heavier balls tend to be more stable when they hit the ground. The outputs of the current work can be used for future analysis of the volleyball game ball for improving its stability.