  There exists continuous demand of improved turbulence models for the closure
of Reynolds Averaged Navier-Stokes (RANS) simulations. Machine Learning (ML)
offers effective tools for establishing advanced empirical Reynolds stress
closures on the basis of high fidelity simulation data. This paper presents a
turbulence model based on the Deep Neural Network(DNN) which takes into account
the non-linear relationship between the Reynolds stress anisotropy tensor and
the local mean velocity gradient as well as the near-wall effect. The
construction and the tuning of the DNN-turbulence model are detailed. We show
that the DNN-turbulence model trained on data from direct numerical simulations
yields an accurate prediction of the Reynolds stresses for plane channel flow.
In particular, we propose including the local turbulence Reynolds number in the
model input.
