Investigation of Interphase Drag Force Affected by Clouded Bubble via a Computational Fluid Dynamics–Discrete Element Method Approach

The effect of the electrostatic charge of particles was investigated in fluidized beds with conductive and nonconductive walls. For this purpose, changes in the wall surface charge density and particle contact time near the wall were examined. Computational fluid dynamics/discrete element method (CFD‐DEM) was utilized to simulate the accumulation of particle and wall‐induced electrostatic charges in a quasi‐two‐dimensional fluidized bed. Particles and walls were initially considered neutral and gained charge with time. The simulations showed that aggregated particles are formed due to the presence of the opposite electrostatic charges. Agglomerates are produced by joining the aggregated particles together. Agglomerates have a layered‐like structure and are made of positive and negative particles. Accumulation of these agglomerates and aggregated particles on the wall forms wall sheeting, which has a layered‐like structure. In other words, particles do not accumulate on the wall individually. A significant accumulation of particles near the wall was seen in the presence of electrostatic charges compared with the neutral bed. Also, the contact time of particles on the wall in the conductive case was more prolonged than in the nonconductive case. The results indicate that the CFD‐DEM approach is a helpful technique for predicting particles behaviour in the presence of the electrostatic charge.

Computational fluid dynamics (CFD) combined with the discrete element method (DEM) are powerful tools for analyzing dense gas–solid flows. However, the computational cost of CFD–DEM will be unfeasibly great when simulating large-scale engineering applications with billions of particles. Accordingly, the coarse-grained (CG) CFD–DEM method is applied to solve this problem. This investigated method replaces several smaller particles with larger ones called parcels, aiming to reduce the number of particles and fully consider the collision of particles between composition parcels and the collision of particles within composition parcels. First, high-speed photography verifies the numerical simulation's reliability. Then, the CG CFD–DEM was used to analyze the transient spatial distribution, transient average velocity, pressure drop, bed height, and the mixing state of particles in a dense gas–solid fluidized bed. The CG CFD–DEM was also compared with the CFD–DEM results, which showed a good agreement with the calculation results and proved the accuracy and applicability of the method. Finally, the computation time of the CG CFD–DEM was evaluated, showing a significant decrease in computation time with an increasing coarse ratio (k). This investigation can provide theoretical reference for the numerical simulation of the CG CFD–DEM method in dense gas–solid flow.

Comprehensive investigation and comparison of TFM, DenseDPM and CFD-DEM for dense fluidized beds

CFD-DEM simulation of fluorination reaction in fluidized beds with local grid and time refinement method

CFD-DEM (computational fluid dynamic-discrete element method) is a promising approach for simulating fluid–solid flows in fluidized beds. This approach generally under-predicts the granular temperature due to the use of drag models for the average drag force. This work develops a simple model to improve the granular temperature through increasing the drag force fluctuations on the particles. The increased drag force fluctuations are designed to match those obtained from PR-DNSs (particle-resolved direct numerical simulations). The impacts of the present model on the granular temperatures are demonstrated by posteriori tests. The posteriori tests of tri-periodic gas–solid flows show that simulations with the present model can obtain transient as well as steady-state granular temperature correctly. Moreover, the posteriori tests of fluidized beds indicated that the present model could significantly improve the granular temperature for the homogenous or slightly inhomogeneous systems, while it showed negligible improvement on the granular temperature for the significantly inhomogeneous systems.

CFD–DEM simulation of a pseudo-two-dimensional spouted bed comprising coarse particles

An augmented coarse-grained CFD-DEM approach for simulation of fluidized beds

Residence time distributions of different size particles in the spray zone of a Wurster fluid bed studied using DEM-CFD

Coupled discrete element method‐computational fluid dynamics (DEM‐CFD) simulations have been performed to study the fluid and particle dynamics in a fluidized‐bed granulator on the scale of individual particles. Simulation of the gas and particle dynamics is combined with heat and mass transfer mechanisms, as the moisture distribution is a key parameter for the functionality of a fluidized‐bed spray granulator. The model allows monitoring the moisture content and temperature of each individual particle as well as the temperature and humidity of the surrounding gas phase. A novel modeling approach is presented to describe the process dynamics of a fluidized bed in full detail for a reference time interval using coupled DEM‐CFD simulations. The motion profile of gas and particles is extrapolated to larger time scales and used for the calculation of heat and mass transfer. Through this multiscale approach, a step forward is taken towards a physically based description of the microprocesses in granulation.

This paper discusses the thermal and exergy efficiency analysis of the hydrothermal liquefaction (HTL) process, which converts sewage sludge into biocrude oil in a continuous plug–flow reactor using a linear Fresnel solar collector. The investigation focuses on the influence of key operational parameters, including slurry flow rate, temperature, pressure, residence time, and the external heat transfer coefficient, on the overall efficiency of biocrude oil production. A detailed thermodynamic evaluation was conducted using process simulation principles and a kinetic model to assess mass and energy balances within the HTL reaction, considering heat and mass momentum exchange in a multiphase system using UDF. The reactor’s receiver, a copper absorber tube, has a total length of 20 m and is designed in a coiled configuration from the base to enhance heat absorption efficiency. To optimize the thermal performance of biomass conversion in the HTL process, a Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) coupling numerical method approach was employed to investigate improved thermal performance by obtaining a heat source solely through solar energy. This numerical modeling approach allows for an in-depth assessment of heat transfer mechanisms and fluid-particle interactions, ensuring efficient energy utilization and sustainable process development. The findings contribute to advancing solar-driven HTL technologies by maximizing thermal efficiency and minimizing external energy requirements.

Super-quadric CFD-DEM simulation of chip-like particles flow in a fluidized bed

Physics-based simulations are often used to model and understand complex physical systems in domains such as fluid dynamics. Such simulations, although used frequently, often suffer from inaccurate or incomplete representations either due to their high computational costs or due to lack of complete physical knowledge of the system. In such situations, it is useful to employ machine learning (ML) to fill the gap by learning a model of the complex physical process directly from simulation data. However, as data generation through simulations is costly, we need to develop models being cognizant of data paucity issues. In such scenarios, it is helpful if the rich physical knowledge of the application domain is incorporated in the architectural design of ML models. We can also use information from physics-based simulations to guide the learning process using aggregate supervision to favorably constrain the learning process. In this article, we propose PhyNet, a deep learning model using physics-guided structural priors and physics-guided aggregate supervision for modeling the drag forces acting on each particle in a computational fluid dynamics-discrete element method. We conduct extensive experiments in the context of drag force prediction and showcase the usefulness of including physics knowledge in our deep learning formulation. PhyNet has been compared with several state-of-the-art models and achieves a significant performance improvement of 7.09% on average. The source code has been made available*.

Particle shape effects on dynamic behaviors in a spouted bed: CFD-DEM study

Fluidization is a well-established technique for processing particulate materials, characterized by intensive interactions between solid particles and the fluid flow. Among the various design parameters of fluidized bed dryers, bed height plays a critical role, directly influencing flow resistance, heat and mass transfer, and consequently, the system’s energy efficiency and overall performance. A thorough understanding of the mechanisms governing fluidized bed dynamics is therefore essential for process optimization. In this study, a three-dimensional gas-solid fluidized bed was simulated using a coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) framework. This approach enables precise tracking of particle motion and distribution, allowing the temporal evolution of bed height to be quantified. Simultaneously, flow characteristics such as gas velocity, pressure, and particle distribution were analyzed under various operating conditions. Before performing a sensitive simulation study, the model was validated against data from previously published studies, showing strong agreement with established empirical correlations and confirming the reliability of the numerical framework. Building on this validation, a parametric study was conducted to examine the effects of key operating variables - namely, inlet gas velocity and bed height - on the hydrodynamic behavior of the system. The obtained results demonstrate that these parameters have a significant impact on flow uniformity and overall bed stability.

The powder mixing for foods is regarded to be unique because tons of products are manufactured through powder mixing processes in food industries; hence there is no room for doubt about the importance of a better understanding of the mixing mechanism. On the other hand, the powder mixing for foods has not been established so far. In this study, a series of simulations of the powder mixing in a liquid are performed using the discrete element method–computational fluid dynamics method. A monodispersed and bimodal mixing systems are computed to evaluate the effect of the particle density and the mixing shaft obliquity in a particular mixing cooker. The monodispersed system was computed purely for a purpose of reference to compare with the bimodal system. By performing a simulation on a monodispersed and bimodal mixing systems with very small density differences between solids as well as solid and liquid, this study provides significant evidence and novel insights into understanding the mixing mechanism of food products. In the monodispersed mixing system, the particles with the density close to water are revealed to be affected by the diffusive mixing which is due to buoyancy and the drag force. Furthermore, an increase in mixing shaft obliquity is shown to improve mixing performance in the monodispersed mixing system. In the bimodal mixing system the effect of the gravitational force is shown to be extremely important for the efficient powder mixing, even if the density difference is very small as 5%. The heavier particles initially positioned in the upper layer are drawn down into the layer of the lighter particles by gravitational force, facilitating mixing and enhancing efficiency. These are new findings which showed unique particle behaviour in mixing systems. This study provides new insights into the specific powder mixing mechanism in a liquid which are frequently encountered in the food industries.