Neural-network-based filtered drag model for gas-particle flows

Yundi Jiang, Jari Kolehmainen, Yile Gu, Yannis G. Kevrekidis, Ali Ozel, Sankaran Sundaresan

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Abstract

Filtered two-fluid model (fTFM) for gas-particle flows require closures for the sub-filter scale corrections to interphase drag force and stresses, the former being more significant. In this study, we have formulated a neural-network-based model to predict the sub-grid drift velocity, which is then used to estimate the drag correction. As a part of the neural network model development effort, we derived a transport equation for drift velocity and then performed a budget analysis to conclude that an algebraic model for drift velocity in terms of the filtered variables that are resolved in a fTFM simulation is adequate, and the model should include the filtered gas-phase pressure gradient as a marker in addition to the filtered particle volume fraction and the filtered gas-solid slip velocity. Both a priori and a posteriori analyses reveal that the present model for drift velocity when used in a fTFM simulation is able to capture the fine-grid simulation results quite well.
Original languageEnglish
Pages (from-to)403-413
Number of pages11
JournalPowder Technology
Volume346
Early online date4 Dec 2018
DOIs
Publication statusPublished - 15 Mar 2019

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    Jiang, Y., Kolehmainen, J., Gu, Y., Kevrekidis, Y. G., Ozel, A., & Sundaresan, S. (2019). Neural-network-based filtered drag model for gas-particle flows. Powder Technology, 346, 403-413. https://doi.org/10.1016/j.powtec.2018.11.092