Modeling transient fluid simulations with proper orthogonal decomposition and machine learning

Page view(s)
8
Checked on Jan 23, 2023
Modeling transient fluid simulations with proper orthogonal decomposition and machine learning
Title:
Modeling transient fluid simulations with proper orthogonal decomposition and machine learning
Other Titles:
International Journal for Numerical Methods in Fluids
Publication Date:
30 June 2020
Citation:
Ooi, C., Le, Q. T., Dao, M. H., Nguyen, V. B., Nguyen, H. H., & Ba, T. (2020). Modeling transient fluid simulations with proper orthogonal decomposition and machine learning. International Journal for Numerical Methods in Fluids, 93(2), 396–410. doi:10.1002/fld.4888
Abstract:
In this work, we present the results obtained from integrating several machine learning models with projection-based reduced order model for modeling the canonical case of flow past a stationary cylinder. We demonstrate how machine learning models can be used to model the time-varying characteristics of the POD coefficients, and that the locally-interpolating models such as regression trees and k-nearest neighbors seem to be better for such models than other models like support vector regression or long-short term memory networks. In addition, our numerical experiments also show that these POD coefficients are most effectively modeled by using their own previous time values, as opposed to the inclusion of high energy POD modes. Last but not least, we demonstrate that these models, although trained on inlet velocities of 0.8, 1.0 and 1.2 m/s, can still predict the POD coefficients of flow fields for inlet velocities of 0.9 and 1.25 m/s, with root mean squared error of under 10%.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - SERC Career Development Award
Grant Reference no. : A1820g0084
Description:
This is the peer reviewed version of the following article: Ooi, C., Le, Q. T., Dao, M. H., Nguyen, V. B., Nguyen, H. H., & Ba, T. (2020). Modeling transient fluid simulations with proper orthogonal decomposition and machine learning. International Journal for Numerical Methods in Fluids, 93(2), 396–410. doi:10.1002/fld.4888, which has been published in final form at http://dx.doi.org/10.1002/fld.4888. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
ISSN:
0271-2091
1097-0363
Files uploaded:


File Size Format Action
pod-ml-clean.pdf 1.47 MB PDF Open
pod-ml-clean.pdf 1.47 MB PDF Open