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The pressure strain correlation plays a critical role in the Reynolds stresstransport modelling. Accurate modelling of the pressure strain correlationleads to proper prediction of turbulence stresses and subsequently the otherterms of engineering interest. However, classical pressure strain correlationmodels are often unreliable for complex turbulent flows. Machine learning basedmodels have shown promise in turbulence modeling but their application has beenlargely restricted to eddy viscosity based models. In this article, we outlinea rationale for the preferential application of machine learning and turbulencedata to develop models at the level of Reynolds stress modeling. As theillustration We develop data driven models for the pressure strain correlationfor turbulent channel flow using neural networks. The input features of theneural networks are chosen using physics based rationale. The networks aretrained with the high resolution DNS data of turbulent channel flow atdifferent friction Reynolds numbers. The testing of the models are performedfor unknown flow statistics at other friction Reynolds numbers and also forturbulent plane Couette flows. Based on the results presented in this article,the proposed machine learning framework exhibits considerable promise and maybe utilized for the development of accurate Reynolds stress models for flowprediction.

Modelling the Pressure Strain Correlation in turbulent flows using Deep Neural Networks