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• W2903431909 abstract "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." @default.
• W2903431909 created "2018-12-11" @default.
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• W2903431909 date "2019-03-01" @default.
• W2903431909 modified "2023-09-29" @default.
• W2903431909 title "Neural-network-based filtered drag model for gas-particle flows" @default.
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• W2903431909 doi "https://doi.org/10.1016/j.powtec.2018.11.092" @default.
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