  The pressure strain correlation plays a critical role in the Reynolds stress
transport modelling. Accurate modelling of the pressure strain correlation
leads to proper prediction of turbulence stresses and subsequently the other
terms of engineering interest. However, classical pressure strain correlation
models are often unreliable for complex turbulent flows. Machine learning based
models have shown promise in turbulence modeling but their application has been
largely restricted to eddy viscosity based models. In this article, we outline
a rationale for the preferential application of machine learning and turbulence
data to develop models at the level of Reynolds stress modeling. As the
illustration We develop data driven models for the pressure strain correlation
for turbulent channel flow using neural networks. The input features of the
neural networks are chosen using physics based rationale. The networks are
trained with the high resolution DNS data of turbulent channel flow at
different friction Reynolds numbers. The testing of the models are performed
for unknown flow statistics at other friction Reynolds numbers and also for
turbulent plane Couette flows. Based on the results presented in this article,
the proposed machine learning framework exhibits considerable promise and may
be utilized for the development of accurate Reynolds stress models for flow
prediction.
