Time-efficient soft computational approach on vibration response of carbon-enriched drive shafts of super-fast race cars

Abstract

Carbon-enriched nanocomposite drive shafts have received considerable attention in super-fast race cars because of their superb material and mechanical properties. thus, for the first time, vibration behavior of drive shafts enriched by three conventional distribution patterns of carbon nanotubes (CNT) in the polymer matrix are explored and reported. A new form in the framework of First-order Timoshenko beam theory is employed. The elasticity moduli of the CNT enriched nanocomposite material are approximated through a refined model of rule of mixtures. Hamilton’s variational principle serves as a way to determine the beam’s equations of motion. Differential quadrature approach (DQA) is employed to predict the system’s frequency for some conditions labeled as the training information. A new soft computation approach based on deep-learning methods employs the training information to adjust a predictor mechanism for obtaining the system’s vibration for any other conditions. This novel methodology provides this opportunity to considerably reduce the computational effort. Validity of the mentioned solution is verified through a comparative scrutinization by low discrepancy between its results and those of the published studies. Importance of these agility in computations would be discerned in cataloging process of the manufactured drive shafts in car industry.