Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates

In the combustion community, the determination of the sub-grid scale contribution to the filtered reaction rate in reacting flows Large Eddy Simulation is an example of closure problem that has been daunting for a long time. We propose a new approach for premixed turbulent combustion modeling based on convolutional neural networks by reformulating the problem of subgrid flame surface density estimation as a machine learning task.

In order to train a neural network for this task, a Direct Numerical Simulation and the equivalent LES obtained by a spatial filtering of this DNS (more details can be found here) is needed.

Description of the dataset

The dataset can be downloaded here. Each of these files corresponds to a time step of a simulation and contains 3 fields :

Filt_8 is the filtered progress variable ,
Filt_grad_8 is the DNS field ,
Grad_filt_8 is the LES field .