Physics-Informed Convolutional Transposed Neural Network for 2-D Reconstruction of Hypersonic Plasma Wakes

Abstract

Deep learning technologies have been widely used in fluid data processing to reconstruct various flow fields. However, due to the complex particle dynamics, relying exclusively on data-driven methods lacks reflection of physical mechanisms. In this article, an electron density reconstruction model of sensor data based on a physics-informed convolutional transposed neural network (PICTNN) is proposed. Employing the continuity equation of plasmas, a physics-informed loss function is constructed to enhance model stability during training through logarithmic maximum normalization. As a validation of the method, based on the density dataset of wakes obtained using the computational fluid dynamics method, the 2-D reconstruction of plasma wakes under different Mach numbers and angles of attack (AOAs) is tested. The results demonstrate excellent preservation of physical features, with Pearson correlation coefficients between the reconstructed data and the computational fluid dynamics simulations reaching up to 0.95. Additionally, this model has been successfully applied to reconstruct 2-D wake distributions from 1-D measurement data. The wake electron density reconstruction model may enhance the effective use of experimental data and extend the measurement capabilities of hypersonic wake devices, offering significant engineering implications.