CFD-DEM Simulation Research Articles

CFD-DEM simulation and experimental investigations on continuous hydraulic sampling of deep-sea polymetallic nodules

The physical sampling to grasp the distribution of deep-sea polymetallic nodules is essential for efficient mining. A hydraulic sampling scheme is proposed that balances low environmental disturbance, high sampling precision, broad coverage, and statistical data viability to address the limitations of existing methods. This paper utilizes a coupled approach of Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) to explore innovative solutions for precise and continuous hydraulic sampling of deep-sea polymetallic nodules. Focusing on the optimization of the double-row nozzle sampling mechanism design, simulations are conducted to analyze the dynamic characteristics of solid-liquid two-phase flow during the hydraulic sampling process under various nozzle configurations and operational water pressures of the pump. Laboratory experiments with the hydraulic sampling mechanism validate the accuracy of the simulation and the effectiveness of the optimization suggestions. The configuration is finalized with a front nozzle diameter of 10 × φ9 mm, a rear nozzle diameter of 10 × φ7 mm, and an initial working water pressure for the pump of 80 kPa. Subsequently, integrated driving-sampling experiments are conducted using a self-propelled tracked vehicle, validating the feasibility of the proposed configuration.

CFD-DEM simulation and experimental validation of air classification for tobacco particles

In cigarette processing, the challenge is particularly significant during the air classification of tobacco and stems due to their similar characteristics. This paper employs computational fluid dynamics (CFD) combined with the discrete element method (DEM) to analyze factors affecting separation efficiency and improve performance. The results were validated through laboratory and production line experiments at a 1:1 scale. The tobacco (length: 0 mm–4.75 mm, diameter: 0.32 mm) and stem (length: 0–25.145 mm, diameter: 1.51 mm) were modeled based on production samples. Findings suggest that particle feeding speed primarily impacts tobacco loss rate, while inlet air velocity mainly influences stem removal rate. Optimizing the chamber structure in the simulation resulted in a 63.66 % improvement in separation efficiency. Airflow streamlines, particle distribution, trajectory, and collision behaviors were discussed to illuminate motion characteristics. The flexible particle model moderately influenced separation efficiency and collision behaviors. These insights enhance the understanding of particle separation and the design of separation devices.

CFD-DEM modeling for optimized liquid distribution in fluidized bed spray granulation

In this work, CFD-DEM simulations of two different fluidized bed granulator geometries were performed, aiming to provide an insight into how the injected liquid behaves within the particle bed and thus an overall better understanding of the granulation process itself. Simulation results were evaluated regarding the fate of sprayed droplets, e.g. deposition on a particle, elutriation from fluidized bed, or collision with apparatus walls. For the deposited droplets, the particle–droplet collisions were tracked in order to obtain a locally resolved deposition rate. Spray parameters as well as geometric specifications of the nozzle setup were varied, investigating a broad range of different process conditions. By optimizing apparatus design, especially the air inlet, an even distribution of the liquid droplets within the fluidized bed and overall higher deposition rate can be achieved and a stable steady state with a well-defined spray zone is reached within a broad range of varied spray parameters.

Optimization research on the layout of scouring pipes in the slurry shield based on CFD-DEM simulation

Optimization research on the layout of scouring pipes in the slurry shield based on CFD-DEM simulation

Pharmaceutical aerosol transport in airways: A combined machine learning (ML) and discrete element model (DEM) approach

The continuum and discrete phase interaction could significantly affect the inhaled particle's transport behaviour. The accurate analysis of continuum and discrete phase interaction needs computational fluid dynamics (CFD) and Discrete Element Method (DEM) simulation, which is computationally expensive. Therefore, this study aims to develop a novel machine learning (ML) prediction model from CFD-DEM data to predict pharmaceutical aerosol transport in airways accurately. This study uses the CFD model for the continuum and DEM for the discrete phases. A soft sphere approach was used to calculate the overlap of the colliding particles. Proper validation was performed to ensure the accuracy of the present model. The CFD-DEM model analysed the particle transport in an idealised and realistic airway model, and different methods were used to analyse the transport behaviour. As the flow rate increased from 15 to 60 lpm, the deposition efficiency (DE) significantly improved due to particle interaction, rising from 20 % to 42 % without interaction and from 50 % to a remarkable 76 % with interaction, demonstrating the critical role of particle interaction in enhancing DE across varying flow rates. During the particle-particle interaction, a stagnation point and a high-pressure zone were observed at the airway model's carinal angle. Finally, a ML prediction model is developed from CFD-DEM data, which accurately predicts the pharmaceutical aerosol deposition in airways. The ML prediction model predicts the deposition pattern for different flow rates and particle sizes without CFD-DEM simulations. The present findings and more case-specific investigation would advance the knowledge of aerosol transport in airways and benefit more efficient targeted drug delivery devices.

Numerical simulation of gasifier optimization for combined hydrogen production and carbon reduction in a chemical looping gasification (CLG) system

Chemical looping gasification (CLG) for combined hydrogen production and carbon capture represents a significant pathway for the clean, low-carbon utilization of traditional fossil fuels. Currently, a significant knowledge gap remains concerning the interplay between reactor configuration optimization and the complex gas–solid interactions in the CLG systems. This study integrates reactive computational fluid dynamics-discrete element method (CFD-DEM) simulations with a coarse-grained model (CGM) to analyze 3D coal-based CLG systems. The spatiotemporal distributions of gas–solid flow dynamics and thermochemical characteristics are revealed. It is found that increasing the gasifier height enhances fuel conversion and hydrogen production but compromises CO2 absorption. Smaller reactor diameters induce excessively rapid gas–solid flows, leading to incomplete fuel conversion and reduced hydrogen yield, while larger diameters cause uneven flows, significantly deteriorating hydrogen production and carbon absorption efficiency. Based on the simulation findings, the optimal height-to-diameter ratio for the gasifier in a CLG system is approximately 28.8, optimizing H2 production while maintaining efficient CO2 absorption. Moreover, the superficial gas velocity has a multifaceted influence on the hydrogen production and carbon reduction performance in the CLG process, necessitating a comprehensive consideration of its effects on fuel reaction time, gas–solid interactions, and other factors.

Identification and characterization of particle clusters in fluidized beds via image processing technique: Method development and assessment

Clusters have a significant impact on gas–solid hydrodynamics, thus have garnered sustained research efforts for several decades. Recently, the image processing technology has been popularly used in characterizing clusters due to its non-invasive nature and high spatiotemporal resolution. This study introduced an innovative method for cluster identification by utilizing non-local means filtering and the maximum between-cluster variance. An exhaustive evaluation of image processing-based identification techniques was conducted using the flow data from CFD-DEM simulations. The approach can accurately reproduce the cluster dynamics throughout the entire bed without the need of manual adjustments, whereas traditional global threshold methods often fall short in reliably predicting clusters with low concentration or larger size. At lower gas velocities, there is a clear correlation between cluster characteristic parameters, while this type of relationship diminishes at higher gas velocities. Furthermore, clusters at different horizontal positions exhibit distinct morphologies and movement patterns.

Research on flow field characteristics of curved pipe in bulk grain cyclone conveying based on gas solid coupling

AbstractIn the transportation of grain particles, elbow wear is a pressing concern. A novel approach to mitigate this involves introducing a lateral air supplement rotation mechanism that enables swirling transportation. We assessed the integration of this device at angles of 45°, 55°, and 65° to the central axis of the main pipeline. While the multiphase flow velocity within the main conduit was held constant at 20 m/s, the lateral device's airflow velocities were tested at 20, 30, and 40 m/s. Utilizing the CFD–DEM simulation for grain particle transportation around bends, our findings indicated that an optimal arrangement was with the lateral device at 55° relative to the main pipeline, with an airflow speed of 30 m/s. This setup fostered a forward particle spiral, drastically diminishing wear on the pipe wall. Experimental evaluations corroborated the simulation's outcomes.

Numerical investigation of heat transfer characteristics of hydrogen flow in a randomly packed bed

In this study, two randomly packed beds with small tube-to-particle diameter (D/d) ratios of D/d = 4 (PB-4) and D/d = 5 (PB-5) are constructed using the discrete element method (DEM). The pore-scale heat transfer characteristics of hydrogen flow at low pore Reynolds numbers (Rep < 100) are investigated using a DEM-CFD simulation approach, with the temperature-dependent thermo-properties of hydrogen being considered. Distinct local channeling flow patterns are revealed by detailed analyses of the flow fields in PB-4 and PB-5. The overall friction factors for both beds are found to agree well with empirical correlations. Notably, a faster spatial temperature increase is exhibited by PB-5, with a larger D/d ratio, as evidenced by a shorter axial distance for fully-heated hydrogen flow compared to PB-4. For the pore-scale heat transfer in the packed beds, the radial temperature profiles are compared at various heights of the packed beds depending on the radial porosity oscillation, in which the wall effect should be especially considered in the near-wall region. Furthermore, the axial pore-scale Nusselt number (Nup,axial) exhibits an inverse fluctuation with the axial porosity oscillation. Finally, the global Nusselt numbers (Nu) in the packed beds are analyzed under different operating conditions, which are in good agreement with empirical correlations.

CatchyCFDEM: Open-source microkinetic modeling of heterogeneous catalysis in Eulerian-Lagrangian CFD applied to oxidative coupling of methane

This work introduces catchyCFDEM, an OpenFOAM-based open-source CFD-DEM framework designed for simulating reactive gas–solid fluidized beds with both gas-phase chemistry and heterogeneous catalytic chemistry. A convective gas–solid heat transfer model is implemented and validated using experimental data gathered in a non-reactive pseudo-2D fluidized bed. Catalytic chemistry including different levels of complexity are included: pseudo-homogeneous, heterogeneous without mass transfer, and heterogeneous with diffusive mass transfer. Verification studies are performed by simulating a packed bed reactor using catchyCFDEM and comparing mass fraction profiles to a 1D plug flow simulation in Cantera. To enhance the computational efficiency of the resource-intensive reactive CFD-DEM simulations, speed-up algorithms such as Particle Agglomeration (PA) and in-situ adaptive tabulation (ISAT) are integrated in catchyCFDEM. As a case study, a gas–solid vortex reactor (GSVR) is simulated using the validated framework. The applicability of the GSVR for the oxidative coupling of methane (OCM) is evaluated based on an isothermal analysis evaluating the influence of several operational and geometrical parameters on the process. Finally, a proof-of-concept adiabatic simulation of the GSVR is presented. The catchyCFDEM framework is made publicly available on GitHub.

Modeling Dense Particle Flow in Multistage and Obstructed Flow Receivers Using High Fidelity Simulations

Particles are a leading contender for next-generation, concentrating solar power technologies, and the design of the particle receiver is critical to minimize the levelized cost of electricity. Falling particle receivers (FPRs) are a viable receiver concept, but many new designs feature complex particle obstructions that include dense discrete phase flows. This creates additional challenges for modeling as particle-to-particle interactions (i.e., collisions) and particle drag become more complex. To improve upon existing modeling strategies, a CFD-DEM simulation capability was created by coupling two independent codes: Sierra/Fuego and LAMMPS. A suitable receiver model was then defined using a traditional continuum-based model for the air and a granular model for the particle curtain. A sensitivity study was executed using this model to determine the relevance of different granular model inputs on important quantities of interest in obstructed flow FPRs: the particle velocity and curtain opacity. The study showed that the granular model inputs had little effect on the particle velocity magnitude and curtain opacity after an obstruction.

Unresolved RBCs: An upscaling strategy for the CFD-DEM simulation of blood flow with deformable cells

Numerical simulation of blood flow is a challenging topic due to the multiphase nature of this biological fluid. The choice of a specific method among the ones available in literature is often motivated by the physical scale of interest. Single-phase approximation allows for lower computational time, but does not consider this multiphase nature. Cell-level simulation, on the other hand, requires high computational resources and is limited to small scales. This work proposes a scale-up approach for cell-level simulation of blood flow, in the framework of unresolved CFD-DEM technique. This method offers the possibility to simulate hundreds of thousands of particles with limited computational effort, but requires specific models for fluid–particle interactions. Regarding blood flow, drag and lift force acting on the red blood cells (RBCs) are responsible for several macroscopic blood characteristics. Despite several correlations available for drag and lift force acting on rigid particles, specific force models for the simulation of deformable particles compatible with RBCs physics are missing. This study employs data obtained from cell-level simulations to derive equations then used in unresolved simulation of RBCs. The strategy followed during the modeling phase is presented, together with the model verification and validation. This approach returns satisfying results when used to simulate blood flow in large-scale channels. Up to half a million RBCs are considered, and computational effort is reported to allow a comparison with other existing methods. Future perspectives include further improvement of the model, such as a deeper understanding of particle–particle interactions.

A Critical Analysis of the CFD-DEM Simulation of Pharmaceutical Aerosols Deposition in Upper Intra-Thoracic Airways: Considerations on Aerosol Transport and Deposition.

The reliability and accuracy of numerical models and computer simulations to study aerosol deposition in the human respiratory system is investigated for a patient-specific tracheobronchial tree geometry. A computational fluid dynamics (CFD) model coupled with discrete elements methods (DEM) is used to predict the transport and deposition of the aerosol. The results are compared to experimental and numerical data available in the literature to study and quantify the impact of the modeling parameters and numerical assumptions. Even if the total deposition compares very well with the reference data, it is clear from the present work how local deposition results can depend significantly upon spatial discretization and boundary conditions adopted to represent the respiratory act. The modeling of turbulent fluctuations in the airflow is also found to impact the local deposition and, to a minor extent, the flow characteristics at the inlet of the computational domain. Using the CFD-DEM model, it was also possible to calculate the airflow and particles splitting at bifurcations, which were found to depart from the assumption of being equally distributed among branches adopted by some of the simplified deposition models. The results thus suggest the need for further studies towards improving the quantitative prediction of aerosol transport and deposition in the human airways.

A sub-grid gas–solid interaction model for coarse-grained CFD–DEM simulations

Computational fluid dynamics has been coupled with coarse-grained discrete element method (CFD–CGDEM) to study industrial-scale gas–solid fluidized beds. However, solving the fluid flow at a grid size of several coarse-grained particles (CGPs) cannot accurately predict the gas–solid interaction, and thus reasonable correction model is required. This study develops a sub-grid model to correct the gas–solid interaction and thereby improve the accuracy of coarse-grid CFD–CGDEM simulation. First, the fluid grids with the size of several CGPs are divided into the auxiliary three-dimensional sub-grids. Then, the CGPs are mapped onto the sub-grids by the weight function to reconstruct the local solid velocities and solid fractions of the sub-grids. After that, the simplified mass and momentum conservation equations are solved to resolve the local gas velocities of sub-grids. Finally, the drag on each CGP is corrected based on the local gas-phase hydrodynamics of sub-grids. Results show that the sub-grid model resolving the local gas velocity in all the three directions works better than that just in the main stream direction. The coarse-grid CFD–CGDEM simulations with this sub-grid model agree well with the experiment for the bubbling, turbulent and rapid fluidized beds. Besides, by solving the simplified conservation equations with graphics processing units, the coarse-grid CFD–CGDEM coupled with the sub-grid model achieves 674 times speedup compared to the fine-grid simulation, demonstrating a much more efficient method to study industrial-scale gas–solid fluidized beds.

Study of oblique granular impingement flow through CFD-DEM simulations and energy consumption statistics

This paper investigates an experimental granular impingement flow system through simulations and energy consumption statistics. With the increase of solid concentration, flow patterns undergo cross flow (dense edge and dilute center), dispersed flow, and mixed-jet flow (dilute edge and dense center) in turn. The influence of gas phase on the flow pattern was investigated. According to the analysis of force and time-averaged velocity, it is revealed that gas-solid interaction will cause energy dissipation of granular flow, which cannot be ignored in specific cases. Then, a flow map is proposed according to both the Archimedes number and solid concentration. In addition, the flow pattern transition and local structure variations can be reflected by energy consumption statistics. Based on the simulations and methods developed in this work, it is promising to deepen the understanding and study of granular impingement flow from the viewpoints of energy consumption and new-type flow map.

Viscosity prediction for dense suspensions of non-spherical particles based on CFD-DEM simulations

Reliable estimation of the viscosity and rheology of dense suspensions formed from non-spherical particles is of high importance for studies of many natural and industrial processes. Still, the complexity of underlying physics makes predicting the viscosity of such suspensions a challenging task, resulting in a lack of models capable of doing so for general suspensions. In this work, we present an approach based on a combination of the computational fluid dynamics (CFD) and discrete element method (DEM) developed for arbitrarily shaped particles and use it to predict viscosity of dense suspensions of spheres, rods, and glitters. Simulation results are compared to available experimental data and commonly used engineering correlations. The developed model can reliably predict suspension viscosity in a wide range of solid volume fractions and particle shapes. The simulations with spherical particles also reveal a shear-thickening trend at increased shear rates, which corresponds to the experimentally observed non-Newtonian behavior.

CFD-DEM modeling and simulation of dynamic filtration over heterogeneous porous medium

The present work presents CFD-DEM results for filter cake formation under dynamic filtration in a highly permeable heterogeneous porous medium conceived as a staggered array of cylinders. The main objective is to evaluate how the compacted bed reduces the flow rate through the medium by varying the Reynolds number, diameter, and specific mass of the particles. CFD-DEM modeling captures relevant phenomena such as solid transport by viscous fluid, packing, and pressure drop imposed by particles. Results indicate that the initial instants of filtering are fundamental to bridge pore throats and build the cake. Higher Reynolds numbers, lighter particles, and smaller particles were more effective as long as the bridge formed by obstruction.

Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force

Qualitative description of stressing energies between grinding beads/media in wet stirred media mills at different operation settings could provide a comprehensive understanding of the process parameter influence. In the current work, a modeling approach for wet stirred media mills is described, employing a coupled CFD-DEM framework. This study employs an immersed boundary methodology to emulate the pin-type stirrer rotation in CFD and utilizes a hydrodynamic lubrication force to model the squeezing out and sucking in of fluid between two grinding bead surfaces. The results indicate the influence of process parameters such as the stirrer rotation speed, bead diameter, and bead filling degree. Key findings include the necessity of coupled CFD-DEM with lubrication (i.e., comparison of results from DEM, and coupled CFD-DEM simulations). Dependence of system dynamics on the fluid properties is numerically analyzed using the properties of water–glycerol mixtures.

Neural network architecture search model for thermal radiation in dense particulate systems

In the CFD-DEM simulation of the many dense particulate systems, particle-scale thermal radiation is an important heat transfer mode under high temperatures. In this work, neural network architecture search model with different layer connection matrix is applied to the view factor regression of the thermal radiation in dense particulate systems. Deep neural networks with 3346 feasible architectures are evaluated by the HyperBand algorithm to find the local optimal solution. Neural architecture search model trained by the big data of the view factor gives a good prediction of the macroscopic radiative properties and it is in general agreement with the empirical correlations and experimental data. The particle–wall radiation decreases strongly with the distance and the maximum interaction depth is about 2.0 times the sphere diameter. The trained deep neural network model provides an efficient data-driven closure to discuss the thermal radiation of the particle–particle and particle–wall interactions in particle bed.

Towards accurate modeling of vibration in CFD-DEM simulations of vibrated gas-fluidized beds without using a moving mesh

Vibrated gas-fluidized beds are widely used industrially, and two main methods exist to simulate them computationally: (i) in a moving reference frame by oscillating gravity and (ii) in a stationary reference frame by moving the distributor. Further, it is unclear whether gas flow in the plenum chamber of a vibrated fluidized bed should be modeled as constant or oscillating. Here, we challenge the accuracy of different potential modeling methods by comparing with experimental results of structured bubbling because these results are deterministic, avoiding the need for comparing via statistically averaged quantities. Results show that modeling a moving distributor and moving sidewalls as physically accurately as possible is important, and modeling the system in the moving reference frame is less accurate than in the stationary reference frame, due to subtle differences. Further, it is more accurate to model the gas flow as constant rather than oscillatory in the plenum chamber.