Journal
AICHE JOURNAL
Volume 66, Issue 6, Pages -Publisher
WILEY
DOI: 10.1002/aic.16973
Keywords
coarse-grid simulation; filtered two-fluid model; fine-grid simulation; fluidization; machine learning; subgrid closure model
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Funding
- National Natural Science Foundation of China [91834303, 21625603, 21776173, U1862201]
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Machine learning (ML) is experiencing an immensely fascinating resurgence in a wide variety of fields. However, applying such powerful ML to construct subgrid interphase closures has been rarely reported. To this end, we develop two data-driven ML strategies (i.e., artificial neural networks and eXtreme gradient boosting) to accurately predict filtered subgrid drag corrections using big data from highly resolved simulations of gas-particle fluidization. Quantitative assessments of effects of various subgrid input markers on training prediction outputs are performed and three-marker choice is demonstrated to be the optimal one for predicting the unseen test set. We then develop a parallel data loader to integrate this predictive ML model into a computational fluid dynamic (CFD) framework. Subsequent coarse-grid simulations agree fairly well with experiments regarding the underlying hydrodynamics in bubbling and turbulent fluidized beds. The present ML approach provides easily extended ways to facilitate the development of predictive models for multiphase flows.
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