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 in the paper) 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 .