A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| author | Pash, Graham Hassanaly, Malik Yellapantula, Shashank |
| author_facet | Pash, Graham Hassanaly, Malik Yellapantula, Shashank |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18729 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks Pash, Graham Hassanaly, Malik Yellapantula, Shashank Fluid Dynamics Machine Learning Data Analysis, Statistics and Probability 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. |
| title | A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks |
| topic | Fluid Dynamics Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2402.18729 |