Artificial Neural Networks Modelling of Wall Pressure Spectra Beneath Turbulent Boundary Layers

TL;DR

This paper introduces an ANN-based model for predicting wall pressure spectra beneath turbulent boundary layers, outperforming traditional models especially under adverse pressure gradients, and includes uncertainty quantification for improved reliability.

Contribution

It presents a novel machine learning approach using ANNs trained on diverse experimental and simulation data, with a methodology to extract boundary layer parameters and analyze model sensitivities.

Findings

ANN outperforms traditional models in adverse pressure gradients

Model generalizes better across a wide range of conditions

Uncertainty quantification identifies regions for data improvement

Abstract

We analyse and compare various empirical models of wall pressure spectra beneath turbulent boundary layers and propose an alternative machine learning approach using Artificial Neural Networks (ANN). The analysis and the training of the ANN are performed on data from experiments and high-fidelity simulations by various authors, covering a wide range of flow conditions. We present a methodology to extract all the turbulent boundary layer parameters required by these models, also considering flows experiencing strong adverse pressure gradients. Moreover, the database is explored to unveil important dependencies within the boundary layer parameters and to propose a possible set of features from which the ANN should predict the wall pressure spectra. The results show that the ANN outperforms traditional models in adverse pressure gradients, and its predictive capabilities generalise better over the range of investigated conditions. The analysis is completed with a deep ensemble approach for quantifying the uncertainties in the model prediction and integrated gradient analysis of the model sensitivity to its inputs. Uncertainties and sensitivities allow for identifying the regions where new training data would be most beneficial to the model's accuracy, thus opening the path towards a self-calibrating modelling approach.