MODELLING WALL-PRESSURE SPECTRA in TURBULENT BOUNDARY LAYERS USING NEURAL NETWORKS

In this work, we model the spectra of wallpressure fluctuations beneath subsonic, supersonic and hypersonic turbulent boundary layers (TBLs) at zero pressure gradient using neural networks (NNs). We collect and compile data pertaining to wall-pressure fluctuation spectra from several experimental and computational studies on TBLs. In contrast to conventional methods of hand-tuning the parameters of a model to fit the available data, the use of modern powerful statistical learning techniques such as neural networks provide an automatic and quick way to fit a model. We explore four different scenarios of making use of the compiled data. In comparison with COMPRA-G, an empirical model recently proposed to account for compressibility effects in TBLs, we achieve a better fit to observed data using the NN model, particularly at low frequencies of the spectra.