LEGI - UMR 5519

Financial support : ANR "SCALES"

This work develops subgrid model techniques and proposes methods of diagnosis for Large Eddy Simulation (LES) of turbulent mixing.Several models from these strategies are thus presented to illustrate these methods.The principle of LES is to solve the largest scales of the turbulent flow responsible for major transfers and to model the action of small scales of flowon the resolved scales. Formally, this operation leads to filter equations describing turbulent mixing. Subgrid terms then appear and must bemodeled to close the equations. In this work, we rely on the classification of subgrid models into two categories. "Functional" models whichreproduces the energy transfers between the resolved scales and modeled scales and "Structural" models that seek to reproduce the exact subgrid termitself. The first major challenge is to evaluate the performance of subgrid models taking into account their functional behavior (ability to reproduce theenergy transfers) and structural behaviour (ability to reproduce the term subgrid exactly). Diagnostics of subgrid models have been enabled with theuse of the optimal estimator theory which allows the potential of structural improvement of the model to be evaluated.These methods were initially involved for the development of a first family of models called algebraic subgrid DRGM for "Dynamic Regularized GradientModel". This family of models is based on the structural diagnostic of terms given by the regularization of the gradient model family.According to the tests performed, this new structural model’s family has better functional and structural performance than original model’s family of thegradient. The improved functional performance is due to the vanishing of inverse energy transfer (backscatter) observed in models of thegradient family. This allows the removal of the unstable behavior typically observed for this family of models.In this work, we then propose the use of the optimal estimator directly as a subgrid scale model. Since the optimal estimator provides the modelwith the best structural performance for a given set of variables, we looked for the set of variables which optimize that performance. Since this set of variablesis large, we use surrogate functions of artificial neural networks type to estimate the optimal estimator. This leads to the "Artificial Neural Network Model"(ANNM). These alternative functions are built from databases in order to emulate the exact terms needed to determine the optimal estimator. The tests of this modelshow that he it has very good performance for simulation configurations not very far from its database used for learning, so these findings may fail thetest of universality.To overcome this difficulty, we propose a hybrid method using an algebraic model and a surrogate model based on artificial neural networks. Thebasis of this new model family ACM for "Adaptive Coefficient Model" is based on vector and tensor decomposition of the exact subgrid terms. Thesedecompositions require the calculation of dynamic coefficients which are modeled by artificial neural networks. These networks have a learning method designedto directlyoptimize the structural and functional performances of ACM. These hybrids models combine the universality of algebraic model with high performance butvery specialized performance of surrogate models. The result give models which are more universal than ANNM.

Publications

Peer-reviewed Publications

2017

Vollant, A., Balarac, G., & Corre, C. E. (2017). Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures. Journal of Turbulence, 18(9), 854–878.

  

2016

Vollant, A., Balarac, G., & Corre, C. E. (2016). A dynamic regularized gradient model of the subgrid-scale stress tensor for large-eddy simulation. Physics of Fluids, 28(2), 025114.

  

2013

Balarac, G., Le Sommer, J., Meunier, X., & Vollant, A. (2013). A dynamic regularized gradient model of the subgrid-scale scalar flux for large eddy simulations. Physics of Fluids, , 075107.

  

Conference Proceedings

2014

Vollant, A., Balarac, G., & Corre, C. E. (2014). Optimal subgrid scale model for passive scalar based on artificial neural network. In 10th European Fluid Mechanics Conference.. Copenhagen, Denmark.

  

Vollant, A., Balarac, G., Geraci, G., & Corre, C. E. (2014). Optimal estimator and artificial neural network as efficient tools for the subgrid-scale scalar flux modeling. In Proceedings of the Summer Program. Stanford, United States.

  

2012

Balarac, G., Le Sommer, J., & Vollant, A. (2012). Subgrid-scale modeling of SGS scalar flux: a regularization of the gradient model. In Proceedings of the Summer Program 2012 (pp. 97–107). Stanford, United States.

  

Vollant, A., Balarac, G., & Corre, C. E. (2012). Passive scalar LES using an optimal estimator as SGS model. In 9th European Fluid Mechanics Conference. Rome, Italy.

  

Ph.D. Theses

2015

Vollant, A. (2015). Evaluation et développement de modèles sous-maille pour la simulation des grandes échelles du mélange turbulent basés sur l’estimation optimale et l’apprentissage supervisé. Ph.D. thesis, Université Grenoble Alpes, .