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The present study extracts human-understandable insights from machine learning (ML)-based mesoscale closure in fluid-particle flows via several novel data-driven analysis approaches, i.e., maximal information coefficient (MIC), interpretable ML, and automated ML. It is previously shown that the solid volume fraction has the greatest effect on the drag force. The present study aims to quantitatively investigate the influence of flow properties on mesoscale drag correction (Hd). The MIC results show strong correlations between the features (i.e., slip velocity (u˜sy∗) and particle volume fraction (ε¯s)) and the label Hd. The interpretable ML analysis confirms this conclusion, and quantifies the contribution of u˜sy∗, ε¯sand gas pressure gradient to the model as 71.9%, 27.2% and 0.9%, respectively. Automated ML without the need to select the model structure and hyperparameters is used for modeling, improving the prediction accuracy over our previous model (Zhu et al., 2020; Ouyang, Zhu, Su, & Luo, 2021).

Particle polydispersity is ubiquitous in industrial fluidized beds, which possesses a significant impact on hydrodynamics of gas–solid flow. Computational fluid dynamics-discrete element method (CFD-DEM) is promising to adequately simulate gas–solid flows with continuous particle size distribution (PSD) while it still suffers from high computational cost. Corresponding coarsening models are thereby desired. This work extends the coarse-grid model to polydisperse systems. Well-resolved simulations with different PSDs are processed through a filtering procedure to modify the gas–particle drag force in coarse-grid simulations. We reveal that the drag correction of individual particle exhibits a dependence on filtered solid volume fraction and filtered slip velocity for both monodisperse and polydisperse systems. Subsequently, the effect of particle size and surrounding PSD is quantified by the ratio of particle size to Sauter mean diameter. Drag correction models for systems with monodisperse and continuous PSD are developed. A priori analysis demonstrates that the developed models exhibit reliable prediction accuracy.

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A reliable kinetic model to describe the effects of various factors on the reaction rate and selectivity of pinene isomerization is developed. Furthermore, computational fluid dynamics (CFD) is applied to simulate the solid–liquid dispersion in reactor. The catalyst TiM is obtained by improving the composition and structure of hydrated titanium dioxide. The kinetic equation of pinene isomerization is deduced based on reaction mechanism and catalyst deactivation model. The kinetic equation of pinene isomerization reaction is fitted, and the results show that the fitted equation is correlated with the experimental data. The rate and selectivity of pinene isomerization reaction are affected by the amount of catalyst, deactivation of catalyst, structure of catalyst, reaction temperature and water content of catalyst. The solid–liquid distribution of the reactor is calculated by computational fluid dynamics numerical simulation, and the solid–liquid dispersion in commercial scale reactor is more uniform than that in lab-scale reactor.

Revealing the evolutionary history of the relationship between humans and Earth will help us understand the spatial and temporal distribution of ancient cultural sites (referred to as ancient sites). This research explored the spatial and temporal distribution of ancient sites in Shaanxi Province (China) from the Han Dynasty to the Tang Dynasty and assessed their correlation with the natural environment and economic patterns using geographic information system (GIS) technologies. The results indicated that (1) the ancient sites in the two periods were mainly centered in Xi'an based on kernel density analysis. The number of ancient sites in the Han to the Northern and Southern Dynasties was greater than that in the Sui and Tang Dynasties. The spatial distribution of ancient sites indicated that more sites are present in northern Shaanxi Province than in the south. (2) The ancient sites in Shaanxi Province were concentrated in the plain area with an elevation of approximately 866 m; the aspects were south, east, and southeast; and the slopes were 0 ~ 3° based on an analysis of the topographic features. (3) The ancient sites were concentrated within 10 km of the river. Fewer ancient sites were distributed with increasing distance from the river, indicating a linear distribution of ancient sites.