A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
- URL: http://arxiv.org/abs/2402.18729v2
- Date: Thu, 25 Jul 2024 03:06:54 GMT
- Title: A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
- Authors: Graham Pash, Malik Hassanaly, Shashank Yellapantula,
- Abstract summary: We employ Bayesian neural networks to capture uncertainties in a reacting flow model.
We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models.
The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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