On the Computational Modeling of Unfluidized and Fluidized Bed Dynamics

2014 ◽  
Vol 136 (10) ◽  
Author(s):  
Lindsey C. Teaters ◽  
Francine Battaglia
Keyword(s):  
Experimental Data ◽  
Pressure Drop ◽  
Multiphase Flow ◽  
Fluidized Bed ◽  
Void Fraction ◽  
Volume Fraction ◽  
Flow Modeling ◽  
Ansys Fluent ◽  
Minimum Fluidization Velocity ◽  
Two Factors

Two factors of great importance when considering gas–solid fluidized bed dynamics are pressure drop and void fraction, which is the volume fraction of the gas phase. It is, of course, possible to obtain pressure drop and void fraction data through experiments, but this tends to be costly and time consuming. It is much preferable to be able to efficiently computationally model fluidized bed dynamics. In the present work, ANSYS Fluent® is used to simulate fluidized bed dynamics using an Eulerian–Eulerian multiphase flow model. By comparing the simulations using Fluent to experimental data as well as to data from other fluidized bed codes such as Multiphase Flow with Interphase eXchanges (MFIX), it is possible to show the strengths and limitations with respect to multiphase flow modeling. The simulations described herein will present modeling beds in the unfluidized regime, where the inlet gas velocity is less than the minimum fluidization velocity, and will deem to shed some light on the discrepancies between experimental data and simulations. In addition, this paper will also include comparisons between experiments and simulations in the fluidized regime using void fraction.

On the Computational Modeling of Unfluidized and Fluidized Bed Dynamics

2012 ◽  
Author(s):  
Lindsey C. Teaters ◽  
Francine Battaglia
Keyword(s):  
Experimental Data ◽  
Pressure Drop ◽  
Multiphase Flow ◽  
Fluidized Bed ◽  
Void Fraction ◽  
Volume Fraction ◽  
Flow Modeling ◽  
Ansys Fluent ◽  
Minimum Fluidization Velocity ◽  
Two Factors

Two factors of great importance when considering gas-solid fluidized bed dynamics are pressure drop and void fraction, which is the volume fraction of the gas phase. It is, of course, possible to obtain pressure drop and void fraction data through experimentation, but this tends to be costly and time consuming. It is much preferable to be able to efficiently computationally model fluidized bed dynamics. In the present work, ANSYS FLUENT is used to simulate fluidized bed dynamics using an Eulerian-Eulerian multiphase flow model. By comparing the simulations using FLUENT to experimental data as well as to data from other fluidized bed codes such as Multiphase Flow with Interphase eXchanges (MFIX), it is possible to show the strengths and limitations of FLUENT with respect to multiphase flow modeling. The simulations described herein will focus on modeling of beds in the unfluidized regime, where the inlet gas velocity is less than the minimum fluidization velocity, and will deem to shed some light on the discrepancies between experimental data and FLUENT results. In addition, this paper will also include comparisons between experimental data and simulation data in the fluidized regime based on void fraction contours and profiles.

A NEW RELATIVE PERMEABILITY MODEL OF UNSATURATED POROUS MEDIA BASED ON FRACTAL THEORY

Fractals ◽  
2020 ◽  
Vol 28 (01) ◽  
pp. 2050002
Author(s):  
KE CHEN ◽  
HE CHEN ◽  
PENG XU
Keyword(s):  
Experimental Data ◽  
Porous Media ◽  
Fractal Dimension ◽  
Multiphase Flow ◽  
Relative Permeability ◽  
Volume Fraction ◽  
Fractal Model ◽  
Accurate Estimation ◽  
Unsaturated Porous Media ◽  
Flow Through

The multiphase flow through unsaturated porous media and accurate estimation of relative permeability are significant for oil and gas reservoir, grounder water resource and chemical engineering, etc. A new fractal model is developed for the multiphase flow through unsaturated porous media, where multiscale pore structure is characterized by fractal scaling law and the trapped water in the pores is taken into account. And the analytical expression for relative permeability is derived accordingly. The relationships between the relative permeability and capillary head as well as saturation are determined. The proposed model is validated by comparison with 14 sets of experimental data, which indicates that the fractal model agrees well with experimental data. It has been found that the proposed fractal model shows evident advantages compared with BC-B model and VG-M model, especially for the porous media with fine content and texture. Further calculations show that water permeability decreases as the fractal dimension increases under fixed saturation because the cumulative volume fraction of small pores increases with the increment of the fractal dimension. The present fractal model for the relative permeability may be helpful to understand the multiphase flow through unsaturated porous media.

scholarly journals SIMULASI PENGARUH KECEPATAN GAS TERHADAP KARAKTERISTIK FLUIDISASI PADA FLUIDIZED BED MENGGUNAKAN METODE CFD

POROS ◽  
2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Asyari Daryus Daryus
Keyword(s):  
Pressure Drop ◽  
Fluidized Bed ◽  
Particle Density ◽  
Gas Velocity ◽  
Experiment Data ◽  
Minimum Fluidization Velocity ◽  
Fluidization Velocity ◽  
Minimum Fluidization ◽  
Gas Fluidization ◽  
Simulation Results

The gas fluidization velocity or superficial gas velocity entering the fluidized bed will affect the fluidization in fluidized bed. If the superficial velocity is below the minimum fluidization velocity then there is no fluidization, and if it is more than it should be then the fluidization characteristic will be different. To obtain the effect of gas fluidization velocity to fluidization characteristics, it had been conducted the research on lab scale fluidized bed using CFD simulation method validated with the experiment data. The simulations used Gidaspow model for drag force and k-ε model for turbulent flow. From the experiments obtained that the minimum fluidization velocity was 0.4 m/s and the pressure drop was around 700 Pa. The simulation results for pressure drop across the bed were close to the experiment data for the gas fluidization velocity equal and bigger than the minimum fluidization velocity. For the velocity below the minimum fluidization velocity, there was the big differences between the simulation results and the experiment, so the simulation results cannot be used. For the fluidization velocity of 0.4 m/s and 0.45 m/s, fluidized bed showed the bubbling phenomena, and the higher velocity showed the bigger bubble. For the fluidization velocity of 0.50 m/s to 0.70 m/s, the fluidized bed showed the turbulent regime. In this regime, the bubble was breaking instead of growing and there was no clear bed surface observed. The simulation result for particle density showed that if the gas velocity was higher, the density of particles at the base of bed was decreasing since many of the particles was moving upwards. The particle density was lower in this regime than that of bubbling regime.

CFD Simulation of Slug Dissipation in an Enlarged Impacting Tee

2019 ◽  
Author(s):  
Mobina Mohammadikharkeshi ◽  
Mazdak Parsi ◽  
Ramin Dabirian ◽  
Ram S. Mohan ◽  
Ovadia Shoham
Keyword(s):  
Experimental Data ◽  
Void Fraction ◽  
Cfd Simulation ◽  
Petroleum Industry ◽  
Production Operation ◽  
Cfd Simulations ◽  
Ansys Fluent ◽  
Transient Model ◽  
Diameter Pipe ◽  
Computational Fluid Dynamics Cfd

Abstract Slug flow, which commonly occurs in the petroleum industry, is not always a desired flow pattern due to production operation problems it may cause in pipelines and processing facilities. To mitigate these problems, flow conditioning devices such as multiphase flow manifolds and slug catchers are used, where dissipation of slugs occurs in downward flow or in larger diameter pipe sections. Tee-junctions are important parts of these flow conditioning devices. In this work, Computational Fluid Dynamics (CFD) simulations are conducted using ANSYS/FLUENT 17.2 to investigate slug dissipation in an Enlarged Impacting Tee-Junction (EIT). An Eulerian–Eulerian MultiFluid VOF transient model in conjunction with the standard k-ε turbulent model is used to simulate slug dissipation in an EIT geometry. The EIT consists of a 0.05 m ID 10 m long inlet, which is connected to the center of a 0.074 m ID 5.5 m long section that forms the EIT branches. Moreover, experimental data are acquired on slug dissipation lengths in a horizontal EIT with a similar geometry as in the CFD simulations. The CFD results include the mean void fraction and cross-sectionally averaged void fraction time series in the EIT for different gas and liquid velocities. These results provide the inlet slug length and dissipation length in the EIT branches. The CFD results are evaluated against the experimental data demonstrating that the slug dissipation occurring in EIT branches can be predicted by simulation.

scholarly journals Swirled injector modeling for cavitating multiphase flow

2019 ◽  
Vol 128 ◽  
pp. 06007
Author(s):  
Bartosz Ziegler ◽  
Jędrzej Mosędrżny ◽  
Natalia Lewandowska
Keyword(s):  
Experimental Data ◽  
Nitrous Oxide ◽  
Heat Exchange ◽  
Multiphase Flow ◽  
Equilibrium Model ◽  
Rocket Engine ◽  
Flow Modeling ◽  
Hybrid Rocket ◽  
Hybrid Rocket Motor ◽  
Significant Magnitude

The goal of this study is to present a comparison between different approaches to multiphase injection modeling of self-pressurized rocket engine propellant. Swirled, tangential orifice injector of nitrous oxide, for an “N” class hybrid rocket motor is the object of the study. A brief descriptionof the injector purpose and geometry is provided, followed by a description of different approaches for flow modeling. Examined techniques range from 0D, Homogeneous Equilibrium Model (HEM) to 3D multiphase with mass and heat exchange between phases. Results of analyses are provided and compared with experimental data. The discrepancies between results are of significant magnitude but expected nature. Co clusions about most feasible approaches for engineering calculations are drawn.

Comparing ANSYS Fluent® and OpenFOAM® simulations of Geldart A, B and D bubbling fluidized bed hydrodynamics

2019 ◽  
Vol 30 (1) ◽  
pp. 93-118 ◽  
Author(s):  
Cesar Martin Venier ◽  
Andrés Reyes Urrutia ◽  
Juan Pablo Capossio ◽  
Jan Baeyens ◽  
Germán Mazza
Keyword(s):  
Fluidized Bed ◽  
Volume Fraction ◽  
Three Dimensional ◽  
Operating Conditions ◽  
Internal Diameter ◽  
Ansys Fluent ◽  
Content Type ◽  
Current State ◽  
Bubbling Fluidized Bed ◽  
Bubble Sizes

Purpose The purpose of this study is to assess the performance of ANSYS Fluent® and OpenFOAM®, at their current state of development, to study the relevant bubbling fluidized bed (BFB) characteristics with Geldart A, B and D particles. Design/methodology/approach For typical Geldart B and D particles, both a three-dimensional cylindrical and a pseudo-two-dimensional arrangement were used to measure the bed pressure drop and solids volume fraction, the latter by digital image analysis techniques. For a typical Geldart A particle, specifically to examine bubbling and slugging phenomena, a 2 m high three-dimensional cylindrical arrangement of small internal diameter was used. The hydrodynamics of the experimentally investigated BFB cases were also simulated for identical geometries and operating conditions using OpenFOAM® v6.0 and ANSYS Fluent® v19.2 at identical mesh and numerical setups. Findings The comparison between experimental and simulated results showed that both ANSYS Fluent® and OpenFOAM® provide a fair qualitative prediction of the bubble sizes and solids fraction for freely-bubbling Geldart B and D particles. For Geldart A particles, operated in a slugging mode, the qualitative predictions are again quite fair, but numerical values of relevant slug characteristics (length, velocity and frequency) slightly favor the use of OpenFOAM®, despite some deviations of predicted slug velocities. Originality/value A useful comparison of computational fluid dynamics (CFD) software performance for different fluidized regimes is presented. The results are discussed and recommendations are formulated for the selection of the CFD software and models involved.

scholarly journals Modeling Average Pressure and Volume Fraction of a Fluidized Bed Using Data-Driven Smart Proxy

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 123 ◽  
Author(s):  
Ansari ◽  
Mohaghegh ◽  
Shahnam ◽  
Dietiker
Keyword(s):  
Multiphase Flow ◽  
Fluidized Bed ◽  
Volume Fraction ◽  
Scale Up ◽  
Computational Cost ◽  
Cfd Simulations ◽  
Large Numbers ◽  
Computational Fluid Dynamics Cfd ◽  
Using Data ◽  
Artificial Neural Network Ann

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.

Effect of Void Fraction on Pressure Drop in Upward Vertical Two-Phase Gas-Liquid Pipe Flow

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Clement C. Tang ◽  
Sanjib Tiwari ◽  
Afshin J. Ghajar
Keyword(s):  
Experimental Data ◽  
Pressure Drop ◽  
Void Fraction ◽  
Gas Velocity ◽  
Two Phase ◽  
Frictional Pressure Drop ◽  
Fraction Increase ◽  
Superficial Gas Velocity ◽  
Two Parameters

Experimental data for the void fraction and two-phase frictional pressure drop from various sources has been compiled and analyzed. The experimental data revealed that at the lower range of superficial gas velocity and void fraction, the variations of the two-phase frictional pressure drop with superficial gas velocity and void fraction are relatively flat. However, as the superficial gas velocity and void fraction increase to higher values, the frictional pressure drop became significantly sensitive to the two parameters. In a situation when the two-phase pressure drop is sensitive to the variation of the void fraction, it is then that the proper and accurate characterization of the void fraction becomes significant. From the experimental data, regions where the pressure drop is sensitive to the variation of the void fraction are identified and evaluated.

On the Modeling of Gas-Solid Fluidization: Which Physics Are Most Important to Capture?

2010 ◽  
Author(s):  
Francine Battaglia ◽  
Jonas A. England ◽  
Santhip Kanholy ◽  
Mirka Deza
Keyword(s):  
Experimental Data ◽  
Pressure Drop ◽  
Packing Fraction ◽  
Walnut Shell ◽  
Minimum Fluidization Velocity ◽  
Fluidization Velocity ◽  
Bed Height ◽  
Minimum Fluidization ◽  
Two Parameters ◽  
Bed Material

Recent studies to predict biomass fluidization hydrodynamics motivated a new study to reassess how to model gas-solid characteristics that capture the same physics as that measured in experiments. An Eulerian-Eulerian multifluid model was used to simulate and analyze gas-solid hydrodynamic behavior of the fluidized beds. The relations for the pressure drop measured at fluidization were used to correct for the bed mass by either adjusting the initial solids packing fraction or initial bed height, two parameters that must be specified in a CFD model. Simulations using sand as the bed medium were compared with experiments and it was found that adjusting the bulk density, or in other words, the initial solids volume packing, correctly predicted the pressure drop measured experimentally, but significantly under-predicted the minimum fluidization velocity. By adjusting the initial bed height to correct for the mass, both the pressure drop and minimum fluidization velocity were successfully predicted. Ground walnut shell and ground corncob were used as biomass media and simulations were performed for two reactor bed diameters by simply adjusting the initial bed height to match the measured pressure drop. All of the simulations correctly predicted the pressure drop curves of the experimental data. Further examination of the simulations and experimental data for walnut shell confirmed that adjusting the bed height was the best approach to model fluidization without artificially altering the physics and retaining the known characteristics of the bed material.

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