Using Delayed Detached Eddy Simulation to create datasets for data-driven turbulence modeling: A periodic hills with parameterized geometry case

Despite the emerging field of data-driven turbulence models, there is a lack of systematic high-fidelity datasets at flow configurations changing continuously with respect to geometrical/physical parameters. In this work, we investigate the possibility of using Delayed Detached Eddy Simulation (DDES) to generate reliable datasets in a significantly cheaper manner compared to the DNS or LES counterparts. To do that, we perform 25 simulations with geometrically-parameterized periodic hills geometries to deal with different hills steepness. We firstly check the accuracy of our results by comparing one simulation with the benchmark case of Xiao et al.. Then, we use such database to train the turbulent viscosity-Vector Basis Neural Network ( ν t -VBNN) data-driven turbulence model. The latter outperforms the classic k − ω SST RANS model, proving that our generated dataset can be useful for data-driven turbulence modeling and opening the opportunity to exploit DDES to create systematic datasets for data-driven turbulence modeling. • A dataset of 25 simulations of the geometrically-parametrized Periodic Hills case is generated using Delayed Detached Eddy Simulation (DDES). • The accuracy of the dataset is carefully tested using benchmark results. • Using DDES enables to create wider datasets than the ones available in literature. • To check the dataset usefulness, the ν t -VBNN turbulence model is trained and tested to improve the classic k − ω SST turbulence model. • The role of several training setting parameters in the ν t -VBNN is discussed