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.

使用贝叶斯神经网络（BNN）对大涡模拟（LES）中的小尺度结构（SFS）进行建模，以捕捉反应流模型中的滤波进度变量标量消失速率的不确定性，并提出了一种将分布之外信息纳入BNN的方法。在包含多种火焰条件和燃料的数据集上，我们对该模型进行先验评估，证明了其有效性。

使用贝叶斯神经网络对反应湍流闭包模型进行先验不确定性量化