Data-driven solution for supersonic aerodynamics analysis of Perovskite solar cells considering the velocity of sound and Mach number

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.