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A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks

arXiv 2402.18729 ↗First posted 28 Feb 2024physics.flu-dyn cs.LG physics.data-ancited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 106 references that are in the map. First posted on arXiv 28 Feb 2024.

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References · 32

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