A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
This work addresses uncertainty estimation for reacting flow models, which is crucial for reliable adoption in computational fluid dynamics, but it is incremental as it applies existing Bayesian methods to a specific domain problem.
The authors tackled the problem of quantifying uncertainty in data-driven closure models for reacting turbulence, using Bayesian neural networks to capture epistemic and aleatoric uncertainties in the filtered progress variable scalar dissipation rate, and demonstrated its efficacy through a priori evaluation on diverse flame datasets.
While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulation (DNS) create opportunities to leverage data-driven modeling techniques. Albeit flexible, data-driven models still depend on the dataset and the functional form of the model chosen. Increased adoption of such models requires reliable uncertainty estimates both in the data-informed and out-of-distribution regimes. In this work, we employ Bayesian neural networks (BNNs) to capture both epistemic and aleatoric uncertainties in a reacting flow model. In particular, we model the filtered progress variable scalar dissipation rate which plays a key role in the dynamics of turbulent premixed flames. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. We also propose a method for the incorporation of out-of-distribution information in a BNN. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.