Conventional and data-driven modeling of filtered drag, heat transfer, and reaction rate in gas-particle flows

This study presents conventional and artificial neural network-based data-driven modeling (DDM) methods to model simultaneously the filtered mesoscale drag, heat transfer and reaction rate in gas-particle flows. The dataset used for developing the DDM is filtered from highly resolved simulations closed by our recently formulated microscopic drag and heat transfer coefficients (HTCs). Results reveal that the filtered drag correction is nearly independent of filter size when including the filtered gas phase pressure gradient. We further find that the filtered HTC correction critically depends on the added filtered temperature difference marker while the filtered reaction rate correction shows weak dependence on the additional markers. Moreover, compared with conventional correlations, DDM predictions agree better with filtered resolved data. Comparative analysis is also conducted between existing HTC corrections and our work. Finally, the applicability of conventional and data-driven models coupled with coarse-grid computational fluid dynamics simulations for pilot-scale (reactive) gas-particle flows is validated comprehensively.

Automated summary

This study introduces conventional and artificial neural network-based data-driven modeling methods for modeling drag, heat transfer, and reaction rate in gas-particle flows, with a focus on the impact of various parameters. The results show that the predictive accuracy of the data-driven model is higher compared to traditional correlations, and the correction of filtered drag is related to filtered temperature difference markers, while the correction of filtered reaction rate shows weak dependence on additional markers.