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Data-driven methods for modelling purposes in fluid mechanics are a promisingalternative given the continuous increase of both computational power anddata-storage capabilities. Highly non-linear flows including turbulence andreaction are challenging to model, and accurate closures for the unresolvedterms in large eddy simulations of such flows are difficult to obtain. In thisstudy, we investigate the use of artificial neural networks for modelling animportant unclosed term namely the unresolved stress tensor, in a highlydemanding turbulent and reacting flow, which additionally includes mean shear.The performance of the neural network-based modelling approach is conducted apriori following a coarsened mesh approach, and compared against thepredictions of eight other classic models in the literature, which include bothstatic and dynamic formulations.

Neural network-based modelling of unresolved stresses in a turbulent reacting flow with mean shear