Multi-scale numerical simulation of fluidized beds: Model applicability assessment

Parallel LES-DEM simulation of dense flows in fluidized beds

The paper presents a study of the flow in a fluidized bed. The analysis is based on measurements made on an experimental stand and computer simulations. Free software packages have been used for simulations. Simulation is done used the most common model for this kind of problem. The first type of model is the Eulerian-Eulerian type and have as their first approximation, the assimilation of the solid phase with a continuous environment. The second category of models is Eulerian-Lagrangean type, they treat the fluid phase as continuous and the solid phase as individual particles or groups of particles “parcels” with similar characteristics. The results of the simulations were compared with the measurements performed on an experimental stand. The experimental stand is composed of a glass column with a diameter of 75mm and a height of 500mm and allows the study of flow in a fluidized bed containing spherical glass beads of 250–500μm in diameter. The results of the simulation were in good agreement whit measured data and in accordance whit the result presented in other specialized studies.

Probabilities of fluidization regimes were determined experimentally using frequency domain analysis of pressure fluctuation data. It was assumed that regime transition does not occur at a distinct gas velocity and fluidization regimes coexist at each gas velocity. Three different flow structures (macro-, meso-, and microstructures) were recognized in a fluidized bed. Macrostructure is represented by bubbles, mesostructures are represented by clusters, and microstructure is represented by particles. These structures are the characteristics of bubbling, turbulent, and fast fluidization regimes. Regime probabilities were determined using power spectrum energies of these structures. Experiments were carried out in a 1 m high and 5 cm inner diameter glass column. Pressure fluctuations were measured using a piezoresistive pressure sensor. Sand particles with various diameters (150, 280, and 490 μm) and fluid catalytic cracking (FCC) particles (73 μm) were used as solids. Probabilities of fluidization regimes were calculated at various gas velocities. A new procedure was proposed for evaluating the regime probabilities versus gas velocity. It was shown that the proposed correlation is in good agreement with the experimental data and is noticeably better than the method proposed in the literature.

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.

This report summarizes the objectives, tasks and accomplishments made during the three year duration of this research project. The report presents the results obtained by applying advanced computational techniques to develop reduced-order models (ROMs) in the case of reacting multiphase flows based on high fidelity numerical simulation of gas-solids flow structures in risers and vertical columns obtained by the Multiphase Flow with Interphase eXchanges (MFIX) software. The research includes a numerical investigation of reacting and non-reacting gas-solids flow systems and computational analysis that will involve model development to accelerate the scale-up process for the design of fluidization systems by providing accurate solutions that match the full-scale models. The computational work contributes to the development of a methodology for obtaining ROMs that is applicable to the system of gas-solid flows. Finally, the validity of the developed ROMs is evaluated by comparing the results against those obtained using the MFIX code. Additionally, the robustness of existing POD-based ROMs for multiphase flows is improved by avoiding non-physical solutions of the gas void fraction and ensuring that the reduced kinetics models used for reactive flows in fluidized beds are thermodynamically consistent.

Acceleration techniques for reduced-order models based on proper orthogonal decomposition

Coupling of CFD-DEM and reaction model for 3D fluidized beds

On the nature of turbulent and fast fluidized beds

Simulations can reduce the time and cost to develop and deploy advanced technologies and enable their rapid scale-up for fossil fuel-based energy systems. However, to ensure their usefulness in practice, the credibility of the simulations needs to be established with uncertainty quantification (UQ) methods. The National Energy Technology Laboratory (NETL) has been applying non-intrusive UQ methodologies to categorize and quantify uncertainties in computational fluid dynamics (CFD) simulations of gas-solid multiphase flows. To reduce the computational cost associated with gas-solid flow simulations required for UQ analysis, techniques commonly used in the area of artificial intelligence (AI) and data mining are used to construct smart proxy models, which can reduce the computational cost of conducting large numbers of multiphase CFD simulations. The feasibility of using AI and machine learning to construct a smart proxy for a gas-solid multiphase flow has been investigated by looking at the flow and particle behavior in a non-reacting rectangular fluidized bed. The NETL’s in house multiphase solver, Multiphase Flow with Interphase eXchanges (MFiX), was used to generate simulation data for the rectangular fluidized bed. The artificial neural network (ANN) was used to construct a CFD smart proxy, which is able to reproduce the CFD results with reasonable error (about 10%). Several blind cases were used to validate this technology. The results show a good agreement with CFD runs while the approach is less computationally expensive. The developed model can be used to generate the time averaged results of any given fluidized bed with the same geometry with different inlet velocity in couple of minutes.

Agglomeration reduces the productivity and the quality of polyethylene fluidized bed. The mathematical model cant characterize the nonlinearity of two-phase flow in the fluidized bed exactly, which make state monitoring and failure diagnosis to inner state of fluidized bed lacking assessment criterion. Acoustic emission sensor could monitor the signals from polyethylene granules impacting on the wall of fluidized beds. Use wavelet packet analysis to process the acoustic emission signals and extract voiceprint feature by MFCC. The feature vector is combined with the MFCC of all sensors. Reduce the dimensionality of vector by PCA and test the feature vector by BP neural network.

The object of this work is to model and simulate a fluidized bed desulfurization reactor by coupling a reactor kinetic model with a fluidized bed model based on two –phase theory(bubbling-bed model) proposed by Kunni and Levenspiel(1968).This model is useful for analysis of a reaction involving gas and solid. It generates the conversion data with respect to reaction time of both reacting gas and solids in a continuous flow(for both gas and solids) in fluidized bed. The flue gas or stack gas from a combustor which normally contains sulfur dioxide mixed with excess air is used as the fluidizing gas with calcium oxide as the fluidized solids . Calcium oxide is quite capable of reacting with 2 SO to effect its removal from the gas phase according to the exothermic reaction :s  CaO + g  SO2 + 2 1g  O2 s  CaSO4 + HeatThe effects of the solid feed rate, SO2 concentration in the flue gas, bed height, bed diameter, particle size, fluidizing velocity and operating temperature on the extent of conversion of both gas and solid were investigated, for a fixed feed rate of flue gas and SO2concentration (50.000 cm3/sec ,3-5% by volume)at temperature range of (950-1000oC)and pressure 1 atm.

The experiments discussed here have been carried out to investigate the mechanism of gas flow and mixing in fluidized beds. Two tracing techniques have been employed in this study. One is referred to as the fine-powder tracing technique, which was developed in the present investigation to observe characteristics of gas flow in a two-dimensional fluidized bed. In the other technique hydrogen was applied as tracer gas to obtain the residence time distribution (RTD) and values of dispersion efficiency of gas. A Dirac delta-function input signal of hydrogen was injected in the beds when the beds were stationary.

Fluidization regimes of dense suspensions of Geldart group A fluidized particles in a high aspect ratio column

Numerical study of gas-solid flow and heat transfer characteristics in fluidized beds using coarse-grained CFD-DEM method

Dynamic characterizations of gas–solid flow in a novel multistage fluidized bed via nonlinear analyses