Akash Haridas

This study evaluates the efficacy of two machine learning (ML) techniques, namely, artificial neural networks (ANNs) and gene expression programing (GEP), that use data-driven modeling to predict wall pressure spectra (WPS) underneath turbulent boundary layers. Different datasets of WPS from experiments and high-fidelity numerical simulations covering a wide range of pressure gradients and Reynolds numbers are considered. For both ML methods, an optimal hyperparameter environment is identified that yields accurate predictions. Despite a higher memory consumption, ANN models are faster to train and are much more accurate than the GEP models, yielding an order of magnitude lower logarithmic Mean Squared Error (lMSE) than GEP. Novel training schemes are devised to address the shortcomings of GEP. These include (a) ANN-assisted GEP to reduce the noise in the training data, (b) exploiting the low- and high-frequency trends to guide the GEP search, and (c) a stepped training strategy where the chromosomes are first trained on the canonical datasets, followed by the datasets with complex features. When compared to the baseline scheme, these training strategies accelerated convergence and resulted in models with superior accuracy (≈30% reduction in the median lMSE) and higher reliability (≈75% reduction in the spread of lMSE in the interquartile range). The final GEP models captured the complex trends of WPS across varying flow conditions and pressure gradients, surpassing the accuracy of Goody's model.