A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populations
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| Publicado en: | arXiv.org (Dec 10, 2019), p. n/a |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Cornell University Library, arXiv.org
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| Resumen: | Phase-averaged dilute bubbly flow models require high-order statistical moments of the bubble population. The method of classes, which directly evolve bins of bubbles in the probability space, are accurate but computationally expensive. Moment-based methods based upon a Gaussian closure present an opportunity to accelerate this approach, particularly when the bubble size distributions are broad (polydisperse). For linear bubble dynamics a Gaussian closure is exact, but for bubbles undergoing large and nonlinear oscillations, it results in a large error from misrepresented higher-order moments. Long short-term memory recurrent neural networks, trained on Monte Carlo truth data, are proposed to improve these model predictions. The networks are used to correct the low-order moment evolution equations and improve prediction of higher-order moments based upon the low-order ones. Results show that the networks can reduce model errors to less than \(1\%\) of their unaugmented values. |
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| ISSN: | 2331-8422 |
| DOI: | 10.1016/j.ijmultiphaseflow.2020.103262 |
| Fuente: | Engineering Database |