  Data-driven turbulence modeling is a newly emerged research area in thermal
hydraulics simulation of nuclear power plant (NPP). The most common CFD method
used in NPP thermal hydraulics simulation is Reynolds-averaged Navier-Stokes
(RANS) method, which still has acknowledged deficiencies not only in the
calculation speed but also in the complexity of choosing turbulence model and
parameters for different flow patterns. Data-driven turbulence modeling aims to
develop a RANS-based method which not only computationally efficient but also
applicable to different flow patterns. To achieve this goal, the first step is
to develop an approach to properly perform RANS for selected flow patterns. In
this work, a machine learning approach is selected to achieve this goal.
  The main purpose of this study is to perform a data-driven approach to model
turbulence Reynolds stress leveraging the potential of massive direct numerical
simulation (DNS) data. The approach is validated by a turbulence flow
validation case: a parallel plane quasi-steady state turbulence flow case.
  The work contains three parts. The first part is database preparation. In
this step, turbulence properties (Reynolds stress) are extracted from DNS
results, which are considered as "physically correct data". Meanwhile, flow
features are extracted from RANS results, which are considered as "data to be
corrected". The second part is surrogate model establishment. In this step, a
data-driven regression function is trained between flow features and turbulence
properties obtained from the previous step. The last part is model validation,
which is applying trained data-driven regression function to a test case to
validate this approach.
