Data-driven subgrid scale modelling with neural networks - Gangu,Vaishnavi (Nanyang Technological University, 2020)
An exploratory study is performed to assess the proficiency of the neural networks in prediction of the non-linear mapping of the closure terms of LES and the coarse grid components in the flow, with a priori assumptions. Two distinct frameworks of convolutional neural networks are built to interpret the relation between the subgrid scale stress and the filtered velocity components. The first approach being the super resolution convolutional neural networks (SRCNN), originally a design for image super resolution, is found to reconstruct the high resolution flow field with a remarkable level of accuracy. Subsequent measures involved the extraction of SGS stress from this high resolution flow field. The second framework involves a direct prediction of the SGS behaviour from the filtered velocity components, exhibiting satisfactory performance. Additional examination of the model architecture encompassed the altering of the convolution kernel width of the intermediate layer. With positive and favourable results, the proposed convolutional neural network frameworks could establish a foundation for the development of potential data-driven subgrid scale models of more complex turbulent flows.