Machine Learning Methods for Data-Driven Turbulence Modeling

As part of a larger effort on data-driven turbulence modeling, this paper investigates machine learning models in their capability to reconstruct the functional forms of spatially distributed quantities extracted from high fidelity simulation and experimental data. Such datasets typically involve very high dimensional feature spaces with sparsely populated and noisy data. A new multiscale Gaussian process regression technique is described and is compared to ‘conventional’ Gaussian process regression and artificial neural networks. All these techniques are applied to the reconstruction of functions arising from Bayesian inference applied to turbulent channel flow and bypass transition. The efficiency, accuracy and effectiveness of each learning algorithm as well as factors that influence their output is assessed. The results highlight the potential of machine learning as an enabling tool in data-driven turbulence modeling.