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.

CFD Simulation of Segregation in Particulate Multiphase Flows With Binary Solids

2005 ◽  
Author(s):  
Zuoxin Hao
Keyword(s):  
Multiphase Flow ◽  
Cfd Simulation ◽  
Volume Fraction ◽  
Solid Volume Fraction ◽  
Primary Objective ◽  
Cfd Simulations ◽  
Separation Processes ◽  
Solid Mixture ◽  
Computational Fluid Dynamics Cfd ◽  
Realistic Prediction

Segregation in particulate multiphase flow with binary solid mixture has extensive applications in industrial separation processes. Up to now there have been few attempts towards numerical simulation of segregation in particulate multiphase flow with binary mixture due to complexity of the problem. In view of this, the primary objective of present work is to simulate the problem by computational fluid dynamics (CFD) and to validate by comparison with experimental measurements. Eulerian-Eulerian approach, incorporating the granular temperature, an essential ingredient in the solids pressure and solids viscosity formulation, was used to model the flow field of multiphase flow and was solved by Fluent 6.0. The CFD simulation results have been validated by experiments of liquid fluidization of binary solid mixtures. Validation results show that CFD simulation predict segregation and solid volume fraction profile precisely, and in addition, it can supply a more realistic prediction of other hydrodynamic features of the multiphase flow, such as velocity vector of all phases and pressure drop. The success of such CFD simulations opens doors for many potential studies.

scholarly journals Hydrodynamic analysis of a liquid-solid fluidized bed via cfd-dem simulations, stability analysis, and tomography

2021 ◽  
Author(s):  
Hussein Zbib
Keyword(s):  
Stability Analysis ◽  
Fluidized Bed ◽  
Volume Fraction ◽  
Particle Volume Fraction ◽  
Percentage Error ◽  
Particle Interactions ◽  
Particle Volume ◽  
Computational Fluid Dynamics Cfd ◽  
The Difference ◽  
Dem Model

A coupled computational fluid dynamics (CFD) and discrete element method (DEM) model was developed to analyze the fluid-particle and particle-particle interactions in a 3D liquid-solid fluidized bed (LSFB). The CFD-DEM model was validated using the Electrical Resistance Tomography (ERT) experimental method. ERT was employed to measure the bed-averaged particle volume fraction (BPVF) of 0.002 m glass beads fluidized with water for various particle numbers and flow rates. It was found that simulations employing the combination of the Gidaspow drag model with pressure gradient and virtual mass forces provided the least percentage error between experiments and simulations. It was also found that contact parameters must be calibrated to account for the particles being wet. The difference between simulations and experiments was 4.74%. The CFD-DEM model was also employed alongside stability analysis to investigate the hydrodynamic behavior within the LSFB and the intermediate flow regime for all cases studied.

Influence of Jordanian zeolite on the performance of a solar still: experiments and CFD simulation studies

2016 ◽  
Vol 16 (6) ◽  
pp. 1700-1709 ◽  
Author(s):  
Yazan Taamneh
Keyword(s):  
Experimental Data ◽  
Phase Change ◽  
Cfd Simulation ◽  
Volume Fraction ◽  
Solar Still ◽  
Cfd Simulations ◽  
Two Phase ◽  
Vof Model ◽  
Computational Fluid Dynamics Cfd ◽  
Good Agreement

Computational fluid dynamics (CFD) simulations were performed for experiments carried out with two identical pyramid-shaped solar stills. One was filled with Jordanian zeolite-seawater and the second was filled with seawater only. This work is focused on CFD analysis validation with experimental data conducted using a model of phase change interaction (evaporation-condensation model) inside the solar still. A volume-of-fluid (VOF) model was used to simulate the inter phase change through evaporation-condensation between zeolite-water and water vapor inside the two solar stills. The effect of the volume fraction of the zeolite particles (0 ≤ ϕ ≤ 0.05) on the heat and distillate yield inside the solar still was investigated. Based on the CFD simulation results, the hourly quantity of freshwater showed a good agreement with the corresponding experimental data. The present study has established the utility of using the VOF two phase flow model to provide a reasonable solution to the complicated inter phase mass transfer in a solar still.

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.

Optimization of CFD Meshing for Stented Vessel Geometries

2014 ◽  
Vol 553 ◽  
pp. 373-378 ◽  
Author(s):  
Azadeh Lotfi ◽  
Tracie J. Barber
Keyword(s):  
Cfd Simulation ◽  
Computational Cost ◽  
Three Dimensional ◽  
Coronary Bifurcation ◽  
Cfd Simulations ◽  
Coronary Stent Implantation ◽  
Bifurcation Stenting ◽  
Computational Fluid Dynamics Cfd ◽  
Blood Flow Patterns ◽  
High Computational Cost

Coronary stent implantation is the most widely used technique currently employed to treat atherosclerosis in coronary artery. Although the optimal technique for bifurcation stenting in terms of clinical outcome is still open to controversy, most previous studies have focused on the single-stenting techniques due to its simpler geometry and easier clinical implantation. While the biomedical environment in a stented coronary bifurcation is extremely challenging to model, Computational Fluid Dynamics (CFD) investigations have been used to study the effect of stent on blood flow patterns, however, in CFD simulation of double-stenting techniques, the presence of two or more stents accentuates the complexity of the geometry and the associated meshes especially in the region where two or multiple stent layers come together. Hence, in this study, complex three-dimensional geometric CFD simulations of a stented vessel have been performed in order to adopt an efficient and optimal meshing method to reduce the high computational cost. In doing so, several meshing strategies were chosen and applied.

Computational Investigation of Hydrodynamics in a Turbulent Fluidized Bed of FCC Particles

2012 ◽  
Vol 550-553 ◽  
pp. 2903-2907
Author(s):  
Jian Chang ◽  
Kai Zhang
Keyword(s):  
Fluidized Bed ◽  
Volume Fraction ◽  
Particle Volume Fraction ◽  
Computational Investigation ◽  
Dense Phase ◽  
Particle Volume ◽  
Turbulent Fluidized Bed ◽  
Computational Fluid Dynamics Cfd ◽  
Cfd Method ◽  
Superficial Gas Velocity

The hydrodynamics in a high and narrow turbulent fluidized bed of FCC particles is investigated by using computational fluid dynamics (CFD) method. The axial bed density, particle volume fraction and particle velocity in the fluidized bed are predicted and compared with the experiments. The results indicate that there exist two different coexisting regions in the bed: a bottom dense and an upper dilute region. As increasing superficial gas velocity, bed density decreases in the dense phase whilst increases in the dilute phase. In this high and narrow turbulent fluidized bed, however, the bed density decreases sharply even in the dense phase because of the wall restriction. The hydrodynamics resembles slug flow at the initial fluidization stage; as a steady fluidization is achieved, the fluidized bed produces larger bubbles than the conventional one.

Aerodynamic prediction of a projectile fitted with fins

2018 ◽  
Vol 28 (5) ◽  
pp. 1218-1236 ◽  
Author(s):  
Cédric Decrocq ◽  
Bastien Martinez ◽  
Marie Albisser ◽  
Simona Dobre ◽  
Patrick Gnemmi ◽  
...  
Keyword(s):  
Long Range ◽  
Computational Cost ◽  
Preliminary Design ◽  
Cfd Simulations ◽  
Content Type ◽  
Speed Up ◽  
Computational Fluid Dynamics Cfd ◽  
Lifting Surfaces ◽  
Time Accuracy ◽  
Low Computational Cost

Purpose The present paper deals with weapon aerodynamics and aims to describe preliminary studies that were conducted for developing the next generation of long-range guided ammunition. Over history, ballistic research scientists were constantly investigating new artillery systems capable of overcoming limitations of range, accuracy and manoeuvrability. While futuristic technologies are increasingly under development, numerous issues concerning current powdered systems still need to be addressed. In this context, the present work deals with the design and the optimization of a new concept of long-range projectile with regard to multidisciplinary fields, including flight scenario, steering strategy, mechanical actuators or size of payload. Design/methodology/approach Investigations are conducted for configurations that combine existing full calibre 155 mm guided artillery shell with a set of lifting surfaces. As the capability of the ammunition highly depends on lifting surfaces in terms of number, shape or position, a parametric study has to be conducted for determining the best aerodynamic architecture. To speed-up this process, initial estimations are conducted thanks to low computational cost methods suitable for preliminary design requirements, in terms of time, accuracy and flexibility. The WASP code (Wing-Aerodynamic-eStimation-for-Projectiles) has been developed for rapidly predicting aerodynamic coefficients (static and dynamic) of a set of lifting surfaces fitted on a projectile fuselage, as a function of geometry and flight conditions, up to transonic velocities. Findings In the present study, WASP predictions at Mach 0.7 of both normal force and pitching moment coefficients are assessed for two configurations. Originality/value Analysis is conducted by gathering results from WASP, computational-fluid-dynamics (CFD) simulations, wind-tunnel experiments and free-flight tests. Obtained results demonstrate the ability of WASP code to be used for preliminary design steps.

Modern Trends in CFD Simulations: Application to GTL Technology

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
Abid Akhtar ◽  
Vishnu K Pareek ◽  
Moses O Tade
Keyword(s):  
Multiphase Flow ◽  
Bubble Column ◽  
Fluid Dynamic ◽  
Scale Up ◽  
Operating Conditions ◽  
Special Focus ◽  
Cfd Simulations ◽  
Multiphase Reactors ◽  
Flow Processes ◽  
Gas To Liquid

Multiphase flow processes are frequently observed in several important reactor technologies. These technologies are found in diverse applications such as in manufacture of petroleum-based fuels and products, conversion of synthesis gas into liquid hydrocarbons (Gas-to-liquid technology), production of commodity chemicals, pharmaceuticals, herbicides, pesticides, polymers etc. Due to the inherent complexity of these processes, the knowledge of fluid dynamic and transport parameters is necessary for development of appropriate reactor models and scale-up rules. It is, therefore, of paramount importance to develop understanding and predictive tools to simulate multiphase flow processes for better and economically viable reactor technologies. In the past, knowledge of hydrodynamics and transport characteristics of multiphase reactors has been interpreted in the form of empirical correlations, which have numerous restrictions in terms of their validity for different operating conditions. Computational fluid dynamics (CFD) simulation, on the other hand, deals with the solution of fluid dynamic equations on digital computers, requiring relatively few restrictive assumptions and thus giving a complete description of the hydrodynamics of these reactors. This detailed predicted flow field gives an accurate insight to the fluid behaviour and can sometimes give information, which cannot be obtained from experiments. These days, due to cheaper computational resources, CFD simulations are becoming economically reliable for modeling of multiphase processes including GTL (Gas-to-liquid) processes. In this paper, a comprehensive review of different multiphase flow simulation approaches has been presented. The recent progress made in hydrodynamic modeling of multiphase reactors, their capabilities and limitations (with special focus on GTL processes) are discussed in detail. Finally, case studies with different simulation approaches (Eulerian-Eulerian and VOF (Volume of fluid) simulations of bubble column reactors operating in different flow regimes) are discussed to demonstrate the power of this emerging research tool.

Current R&D Challenges for Fluidized Bed Processes in the Refining Industry

2009 ◽  
Vol 7 (1) ◽  
Author(s):  
Thierry A Gauthier
Keyword(s):  
Multiphase Flow ◽  
Fluidized Bed ◽  
Large Scale ◽  
New Technologies ◽  
Scale Up ◽  
Theoretical Models ◽  
Refining Industry ◽  
Extensive Literature ◽  
New Concepts ◽  
Complex Phenomena

Fluidized bed processes are widely used in the refining industry, mostly for conversion applications (e.g. fluid catalytic cracking, fluid coking, residue hydroconversion, Fischer-Tropsch Synthesis, etc.). These are large scale processes operating under severe conditions. Fluidized bed processes involve many complex phenomena that need to be considered in order to ensure proper design, operation and reliability. There have been thousands of publications over many years in the field of fluidization, but some of the fundamentals of fluid-particle flows still remain to be clarified. As a consequence, scale-up and industrialization of new technologies or processes remain a difficult, challenging and risky task.IFP is deeply involved with fluidized bed processes used in the refining industry. Over the last twenty years, industrial developments and PhD studies were conducted to explore new concepts, to develop new technologies, to scale-up hydrodynamics, to understand and quantify key phenomena. This paper discusses R&D practices in the field and current challenges encountered, mostly based on IFP experience. It does not intend, however, to provide an extensive literature review of all topics addressed in this paper.Interactions between particles, multiphase flow and reactor geometries are complex issues in fluidized beds. Therefore, experimentation is required to study new concepts such as downflow systems, complex phenomena such as vaporization of droplets in contact with gas-particle systems. The design of the experiment and the development of appropriate instrumentation are never simple and in the absence of simple similarities, anticipation of the main flow features is unavoidable. Modeling of results is then mandatory in order to translate results to industrial perspectives. Over the last 15 years, CFD has appeared as a promising tool to describe multiphase flow phenomena in complex geometries. Unfortunately, the lack of theoretical models to describe gas particle flow, at least for Group A powders, still leads researchers to conduct experiments to validate simulations or to adjust the gas-particle closure equations to validate results. Furthermore, observation in industrial units, during start-up or under steady conditions, when possible, greatly aids in validating research efforts and methodology.Despite its maturity, our industry is moving forward. There are ongoing developments in the energy and fuels market as well as in environmental fields, but also in the scientific background available to describe multiphase flow. Therefore, evolutionary R&D in the field is still needed to progress in the description of complex phenomena in order to optimize reactors and technologies and to face the changes of our industry.

scholarly journals A Data Augmentation-Based Technique for Deep Learning Applied to CFD Simulations

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1843
Author(s):  
Alvaro Abucide-Armas ◽  
Koldo Portal-Porras ◽  
Unai Fernandez-Gamiz ◽  
Ekaitz Zulueta ◽  
Adrian Teso-Fz-Betoño
Keyword(s):  
Fluid Dynamics ◽  
Turbulent Flows ◽  
Input Data ◽  
Data Augmentation ◽  
Stokes Equations ◽  
Computational Cost ◽  
Navier Stokes ◽  
Cfd Simulations ◽  
Navier Stokes Equations ◽  
Computational Fluid Dynamics Cfd

The computational cost and memory demand required by computational fluid dynamics (CFD) codes simulations can become very high. Therefore, the application of convolutional neural networks (CNN) in this field has been studied owing to its capacity to learn patterns from sets of input data, which can considerably approximate the results of the CFD simulations with relative low errors. DeepCFD code has been taken as a basis and with some slight variations in the parameters of the CNN, while the net is able to solve the Navier–Stokes equations for steady turbulent flows with variable input velocities to the domain. In order to acquire extensive input data to the CNN, a data augmentation technique, which considers the similarity principle for fluid dynamics, is implemented. As a consequence, DeepCFD is able to learn the velocities and pressure fields quite accurately, speeding up the time-consuming CFD simulations.

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