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The vertical pressure lead to increased airflow resistance through the grain bulk, which affected the efficiency of ventilation and drying. The effects of vertical pressures at 50, 150, and 250 kPa on the pressure drop characteristics of soybeans were studied using experiment and numerical simulation. The random packing and different compression states for soybean packed beds were generated by the Discrete Element Method (DEM). The Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) were coupled to investigate the radial velocity and pressure drop of soybean bulk. The simulation results showed that the radial porosity had an oscillating distribution, and the radial average dimensionless velocity was consistent with the distribution trend of porosity. The increase in vertical pressure causes a decrease in porosity and an increase in local velocity. The PathFinder code was used as a supplementary method to calculate the pore path and pore characterization parameters, and the resistance coefficient term in the Forchheimer equation was determined. The compression of soybeans measured by the experiment mainly occurred within two hours after loading. The pressure drop of soybeans increased with the vertical pressure, with the average pressure drop at vertical pressures of 50, 150, and 250 kPa being 36%, 57%, and 92% higher than the uncompressed state (0 kPa). The pressure drop of soybeans calculated by the DEM-CFD method and the Forchheimer equation under different vertical pressures were in close agreement with the experimental results, and an average relative difference was found to be less than 10%. These results provide guidance for estimating the pressure drop of soybeans at different grain depths.

ABSTRACT:In order to predict wind loads on building structures in the atmospheric boundary layer (ABL) employing large eddy simulation (LES), the primary task would be to generate proper turbulent inflow boundary condition (IBC), which is one of the main influence factors for LES. The objective of this paper is to present a simple IBC generation method to improve the LES predictions for building loads in the ABL using the commercial code Fluent. The proposed method is a combination of equilibrium IBC expressions for Reynolds averaged NavierStokes (RANS) equations with the random flow generation (RFG) procedure, which is used in Fluent. The equilibrium IBC is used for the solution of steady-state RANS to obtain information for the RFG procedure. In order to verify the efficiency of the present method, the simulation of wind flow field around a single tall building was carried out by adopting the above computing process. Detailed comparisons between the simulated and the experimental results show that the present method was verified as being applicable to a certain extent in predicting wind loads on buildings.

Fixed‐bed drying of grains is a widely used method for determining temperature and humidity changes and pressure drop characteristics during aeration. In this study, an accurate particle‐resolved computational fluid dynamics (CFD) numerical model was developed to investigate the flow and heat transfer in fixed beds of soybean, wheat, and maize grains. A randomly filled non‐spherical grain fixed bed structure was generated using the discrete element method (DEM), and the contact areas between the packed particles are automated. The distribution of grain particles near the wall surface was approximately circular, and the circle became more regular with increase grain sphericity. Compared with the fixed beds of soybean and maize, there was no significant stratification in the fixed beds of wheat, and the difference between the peaks and troughs of porosity oscillations was smaller. Detailed particle‐resolved CFD simulations of the flow and heat transfer in fixed beds with different grains were performed. The results showed that the pressure drop results of the DEM–CFD simulation of different grain shapes were in good agreement with the Nemec–Levec equation and experimental results. The velocity and temperature distribution in the fixed bed have obvious non‐uniform distribution characteristics, which are more visible in the maize pile. Regions with high porosity have a higher average velocity, and the temperature near the wall is higher than that at the center when the heat transfer is unbalanced. The DEM‐CFD model includes the heat conduction process between the fluid and solid phases, which is consistent with the experimental temperature results. Practical Applications In this work, an accurate particle‐resolved computational fluid dynamics numerical model was developed to investigate the flow and heat transfer in fixed beds of soybean, wheat, and maize grains. The randomly filled non‐spherical grain fixed bed structure is generated by the discrete element method (DEM), and the contact areas between the packed particles are automated treated. The pressure drop and heat transfer processes of different grains were measured using the fixed bed drying experimental platform. The results show that the pressure drop results of DEM–CFD simulation of different grain shapes are in good agreement with Nemec–Levec equation results and experimental results. The DEM‐CFD model includes the heat conduction process between fluid and solid phases, which is in close agreement with the experimental temperature results.