Abstract

The dynamic response of concrete structures reinforced with nanocomposites to supersonic airflow represents a critical aspect in aerospace and defense applications, necessitating accurate predictive models for enhanced structural integrity and performance. In this study, we introduce innovative deep neural networks (DNNs) as a novel approach to predict the dynamic behavior of such structures under supersonic airflow conditions. Traditional modeling techniques often face challenges in capturing the intricate interactions between material properties, structural geometry, and airflow dynamics, particularly in the presence of nanocomposite reinforcements. DNNs offer a promising solution by leveraging their ability to learn complex patterns and nonlinear relationships from extensive datasets. This paper presents a comprehensive framework for developing and deploying DNN-based models for dynamic prediction, encompassing network architecture design, training strategies, and data preprocessing techniques tailored to the unique characteristics of nanocomposite-reinforced concrete structures. Through a series of case studies and comparative analyses, we demonstrate the effectiveness and accuracy of DNNs in capturing the dynamic response of such structures to supersonic airflow, including phenomena such as vibration, flutter, and aerodynamic instability. Furthermore, we discuss the potential advantages and challenges associated with the adoption of DNNs in aerospace and defense applications, including model interpretability, computational efficiency, and data requirements. Finally, we outline future research directions and opportunities for further advancing the application of DNNs in addressing dynamic challenges in aerospace engineering and beyond.