A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populations
Abstract
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.
Additional Information
© 2020 Elsevier Ltd. Received 10 December 2019, Revised 21 February 2020, Accepted 2 March 2020, Available online 6 March 2020. The US Office of Naval Research supported this work under MURI grant N0014-17-1-2676.Attached Files
Submitted - 1912.04450.pdf
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Additional details
- Eprint ID
- 101766
- Resolver ID
- CaltechAUTHORS:20200309-084519190
- Office of Naval Research (ONR)
- N0014-17-1-2676
- Created
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2020-03-09Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field