Link youtube : https://youtu.be/IaxXKVZ2cPM
In a century defined by climate change and data abundance, combustion is moving towards new opportunities created by the digital revolution. Large eddy simulations (LES) of full-scale practical combustion systems are becoming tractable, but their predictive power hinges on the accuracy of the subgrid-scale (SGS) models that account for unresolved turbulent combustion physics. Deep learning (DL) has recently been used to train data-driven SGS models that achieve excellent accuracy in a priori tests. Yet, there are still hardly any applications of DL SGS models in LES of practical combustion systems. This work investigates three elements that must be addressed to enable the adoption of deep learning in practical LES of turbulent premixed combustion: evaluating DL models on high Reynolds test cases, ensuring their ability to generalize beyond their training configuration, and implementing a computationally efficient integration of DL models to high-performance LES solvers. Three DL models that gradually include each of these elements are developed. They are based on U-Net convolutional neural networks (CNNs) trained on downsampled filtered snapshots of direct numerical simulations. First, a model for the total flame surface density is trained on the R2 high Reynolds turbulent jet flame. Excellent a priori generalization to higher Reynolds numbers and to LES snapshots is observed, and insights on the inner workings of the model are provided. Second, a CNN model for the SGS variance of the progress variable is trained on a statistically planar turbulent flame. With a Pfitzner source term formulation and a beta probability density function closure, it is able to accurately predict a priori the SGS variance and the filtered reaction rate on the R2 jet flame, thus demonstrating its ability to generalize to new configurations. Third, the AVBP-DL coupling strategy is developed to enable DL models to be queried by the AVBP solver with negligible computational overhead. Finally, the Masri vented obstructed explosion test case is used to test a posteriori a CNN model for the SGS wrinkling factor trained on the statistically planar turbulent flame. The model predicts the right peak overpressure, but this results from a compensation between excessive wrinkling in the initial laminar phase and insufficient wrinkling in the critical turbulent propagation stage. Several attempts to correct this behavior are then explored.
Denis VEYNANTE – Research director – EM2C – Referee
Antonio ATTILI – Assistant Professor – Univ. Edinburgh – Referee
Michael PFITZNER – Univ.-Prof. Dr. rer. nat. – UniBw – Examiner
Pascale DOMINGO – Research director – CORIA – Examiner
Corentin LAPEYRE – Senior researcher – CERFACS – Advisor
Thierry POINSOT – Research director – IMFT – Advisor