A curated dataset for data-driven turbulence modelling

Abstract

The recent surge in machine learning augmented turbulence modelling is a promising approach for addressing the limitations of Reynolds-averaged Navier-Stokes (RANS) models. This work presents the development of the first open-source dataset, curated and structured for immediate use in machine learning augmented turbulence closure modelling. The dataset features a variety of RANS simulations with matching direct numerical simulation (DNS) and large-eddy simulation (LES) data. Four turbulence models are selected to form the initial dataset: $k$-$\epsilon$, $k$-$\epsilon$-${\varphi }_t$-$f$, $k$-$\omega$, and $k$-$\omega$ SST. The dataset consists of 29 cases per turbulence model, for several parametrically sweeping reference DNS/LES cases: periodic hills, square duct, parametric bumps, converging-diverging channel, and a curved backward-facing step. At each of the 895,640 points, various RANS features with DNS/LES labels are available. The feature set includes quantities used in current state-of-the-art models, and additional fields which enable the generation of new feature sets. The dataset reduces effort required to train, test, and benchmark new models. The dataset is available at https://doi.org/10.34740/kaggle/dsv/2044393 .

21 pages, 20 figures, 15 tables. Article yet to be submitted to a journal

References in corpus (9)

• A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
• Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework
• Perspectives on Machine Learning-augmented Reynolds-averaged and Large Eddy Simulation Models of Turbulence
• Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
• Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
• Data-Driven Modelling of the Reynolds Stress Tensor using Random Forests with Invariance
• Direct Numerical Simulation of a separated channel flow with a smooth profile
• Neural Network Models for the Anisotropic Reynolds Stress Tensor in Turbulent Channel Flow
• Turbulence closure modeling with data-driven techniques: physical compatibility and consistency considerations

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