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Abstract Due to the use of Perovskite solar cells (PSC) in aerospace applications, so, in this work for the first time, aerodynamic flutter and vibrational analysis of PSC are presented. For modeling the PSCs aerodynamic motion in the mathematical domain, the variational differential quadrature (VDQ) technique is used as a numerical operator for the discretization of various domains. Also, due to improving the stability of the PSC, carbon nanotubes with various distribution patterns as the reinforcement of the metal layer are presented. In this work for modeling the aerodynamics behavior, we use Krumhaar’s modified supersonic piston theory incorporating curvature influence, to simulate the aerodynamic stiffness and damping matrices. Also, for modeling the size effect, a modified couple stress theory with only one length scale factor is suggested. Data-driven solution with the aid of deep neural network with independent variables, input, hidden, and output layers, activation function, neural network’s intrinsic parameters, loss function, and hidden neurons is presented to solve the current applicable structure. The results show that the stability of the system is substantially impacted by the value of the yaw angle, and the link between Mach number and stability is heavily reliant on the value of the yaw angle. Another important outcome is that designing a spherical PSC and increasing its stability should have special attention to the a/b parameter. Also, future engineering research should have special attention that spherical solar cell has the highest natural frequency than other types of solar cells. Other important outcomes of the innovative PSC are presented in the results section for future handbooks on solar cells as sustainable energy. [ABSTRACT FROM AUTHOR]

Abstract Recent years have seen significant research on oscillations in aircraft structures. A self-excited oscillation known as flutter is known to wear down aircraft equipment. So, in this work, the graphene nanoplatelets reinforced composite (GPLRC) micropanel’s nonlinear aerodynamics are modeled in this paper, and the nonlinear aerodynamics properties are validated using an artificial neural network technique. The axial direction and the material characteristics of the current system are graded. The relationship between the input layers of composite weight fraction, mode number, and slenderness ratio and the output layer of nonlinear vibrations derived from mathematical simulation has been estimated using an artificial neural network model. As a training algorithm, Levenberg–Marquardt back-propagation is employed. This study is new in that it attempts for the first time to estimate the nonlinear vibration properties of micropanels built of axially graded GPLRC material using the aforementioned input layer. Without the need to solve any differential equations or go through time-consuming experimental procedures, the suggested artificial neural network model can forecast linear or nonlinear natural frequencies. The findings demonstrate that axially graded GPLRC micropanel linear/nonlinear vibration problems can be effectively addressed using artificial intelligence techniques. Future vibration analyses of the microstructures for engineering reasons can use the provided mode. [ABSTRACT FROM AUTHOR]