TITLE: 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: $k$-$\varepsilon$, $k$-$\varepsilon$-$\phi_t$-$f$,
$k$-$\omega$, 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 .
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: $k$-$\varepsilon$, $k$-$\varepsilon$-$\phi_t$-$f$,
$k$-$\omega$, 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 .
A curated dataset for data-driven turbulence modelling
http://arxiv.org/abs/2103.11515v1