A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populations
Name
1912.04450.pdf
Description
Submitted version
Size
541.38 KB
Format
Adobe PDF
Checksum (MD5)
4fa2c3e15bcfa5bff8fcce6bd1f4451b
Author(s)
Bryngelson, Spencer H
Charalampopoulos, Alexis
Sapsis, Themistoklis P
Colonius, Tim
Date Issued
2020
Journal
International Journal of Multiphase Flow
Publisher
Elsevier BV
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.ijmultiphaseflow.2020.103262
