Data-driven closure modeling for scale resolving PANS simulations in flows with coherent structures

For complex turbulent flows with largescale instabilities and coherent structures, both traditional and data-driven Reynolds-averaged Navier-Stokes (RANS) methods are inherently unsuitable. Scale resolving simulations (SRS) such as the partially averaged Navier-Stokes (PANS) method are more appropriate as they resolve the unsteady and coherent scales of motion. The objective of this work is to develop a sub-filter stress neural network for SRS methods using high-fidelity data. The three main features of the new model development are: (i) improved incorporation of the unsteady flow features of the high-fidelity data into the closure model; (ii) the features and input tensors used in the training are taken from model computations resulting in greater overall consistency; and (iii) explicit dependence of the closure on the filter size or degree of resolution. The closure development is performed in the context of PANS approach, but the technique can be extended to other SRS methods. The potential improvement of the proposed method over over existing approaches is clearly established.