Deep learning reconstruction of pressure fluctuations in supersonic shock-boundary layer interaction

Abstract

The long short-term memory deep-learning model is applied to supersonic shock-boundary layer interaction flow. The study aims to show how near-wall pressure fluctuations can be reconstructed from reduced (under-sampled) datasets of pressure signals. Predicting pressure fluctuations from reduced datasets could allow predictions using less expensive simulations and experiments. The training of the deep learning model is based on direct numerical simulations of supersonic ramp flows, focusing on the regions upstream of and around the shock-boundary layer interaction region. During the pre-processing stage, cubic spline functions increase the fidelity of the sparse signals and feed them to the long-short memory model for an accurate reconstruction. Comparisons are also carried out for different sparsity factors and assess the model's accuracy both qualitatively through the pressure signals and quantitatively using the root mean square error and the power spectra. The deep learning predictions are promising and can be extended to include other aerodynamic or aeroelastic parameters of interest.