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The Reynolds-averaged Navier-Stokes (RANS) equations require accuratemodeling of the anisotropic Reynolds stress tensor. Traditional closure models,while sophisticated, often only apply to restricted flow configurations.Researchers have started using machine learning approaches to tackle thisproblem by developing more general closure models informed by data. In thiswork we build upon recent convolutional neural network architectures used forturbulence modeling and propose a multi-task learning-based fully convolutionalneural network that is able to accurately predict the normalized anisotropicReynolds stress tensor for turbulent duct flows. Furthermore, we also explorethe application of curriculum learning to data-driven turbulence modeling.

Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow