scholarly journals CFD & Safety Factors

2013 ◽  
Vol 135 (02) ◽  
pp. 30-35
Author(s):  
Robert A. Leishear
Keyword(s):  
Experimental Validation ◽  
Cfd Modeling ◽  
Correction Factors ◽  
Safety Factors ◽  
Additional Result ◽  
Complex Processes ◽  
Cfd Models ◽  
Fluid Velocities ◽  
Computational Fluid Dynamics Cfd ◽  
The Times

This article presents a research that demonstrates the need for experimental validation of computational fluid dynamics (CFD) models for complex processes, such as blending. An additional result of the study is that it provided researchers a better understanding of how to use CFD models in general. The principle for blending is the same for all blender-pump designs: the business end of a centrifugal pump will be submerged in the salt solutions in the tank. Lab researchers found that, although CFD provided good estimates of an average blending time, experimental blending times varied significantly from the average. The issue of experimental uncertainty is inherent in CFD modeling as well as in many empirical equations used for modeling and design methods. In order to bring all of this research together, the process variables investigated were the fluid velocities in the tanks and the times required to blend the fluids. The large scatter in experimental data shows that large errors can be obtained from CFD models in the absence of experimental correction factors.

Improvements on a Newton-Krylov Based Solver for CFD Models Using Finite Rate NOx Chemistry

2004 ◽  
Author(s):  
Qing Tang ◽  
Martin Denison ◽  
Mike Maguire ◽  
Mike Bockelie ◽  
Jyh-Yuan Chen
Keyword(s):  
Electric Utility ◽  
Iteration Scheme ◽  
Cfd Modeling ◽  
Finite Rate ◽  
Modeling Tool ◽  
Krylov Method ◽  
Cfd Models ◽  
Reduced Mechanism ◽  
Computational Fluid Dynamics Cfd ◽  
Matrix Free

In this paper, we describe our progress on improving the performance of a newly developed Computational Fluid Dynamics (CFD) modeling tool, which uses reduced chemical kinetics mechanisms to model the finite rate chemistry effects and solves the resulting system of stiff partial differential equations with a matrix-free Newton-Krylov method. A multi-grid based preconditioner and a Newton iteration scheme have been implemented in the Newton-Krylov solver and the reduced mechanism module, respectively, to replace the original Picard based preconditioner and the point iteration scheme for steady state species evaluation. Preliminary tests of the improved modeling tool have been conducted using simple hotbox and a full-scale, coal fired electric utility boiler, and shown very promising results in terms of the accuracy, robustness, and efficiency of the new tool.

scholarly journals CFD Models as a Tool to Analyze the Performance of the Hydraulic Agitation System of an Air-Assisted Sprayer

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 769 ◽  
Author(s):  
Jorge Badules ◽  
Mariano Vidal ◽  
Antonio Boné ◽  
Emilio Gil ◽  
F. Javier García-Ramos
Keyword(s):  
Fluid Velocity ◽  
Water Levels ◽  
Correct Prediction ◽  
Cfd Simulations ◽  
Cfd Models ◽  
Fluid Velocities ◽  
Computational Fluid Dynamics Cfd ◽  
Relative Errors ◽  
Setting Parameters ◽  
System Water

A computational fluid dynamics (CFD) model of the fluid velocities generated by the agitation system of an air-assisted sprayer was developed and validated by practical experiments in a laboratory. The model was developed considering different settings of the agitation system: Three water levels in the tank (1000, 2000, and 3000 L); two different numbers of active nozzles (2 or 4); and three working pressures of the agitation circuit (8, 10, or 12 bar). Actual measurements of the fluid velocity into the tank were taken using an acoustic Doppler velocimeter (ADV). CFD simulations made it possible to estimate fluid velocities at 38% of the measuring points with relative errors of less than 30%. Additionally, the CFD models have allowed the correct prediction of the general behavior of the fluid in the tank considering mean velocities depending on the setting parameters of the agitation system (water level in the tank, hydraulic circuit pressure, and number of active nozzles).

scholarly journals Computational Fluid Dynamics Modeling of Liver Radioembolization: A Review

2021 ◽  
Author(s):  
Jorge Aramburu ◽  
Raúl Antón ◽  
Macarena Rodríguez-Fraile ◽  
Bruno Sangro ◽  
José Ignacio Bilbao
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Point Of View ◽  
Cfd Modeling ◽  
Dynamics Modeling ◽  
Cfd Simulations ◽  
Computational Fluid Dynamics Modeling ◽  
Cfd Models ◽  
Computational Fluid Dynamics Cfd ◽  
Intraarterial Therapy

AbstractYttrium-90 radioembolization (RE) is a widely used transcatheter intraarterial therapy for patients with unresectable liver cancer. In the last decade, computer simulations of hepatic artery hemodynamics during RE have been performed with the aim of better understanding and improving the therapy. In this review, we introduce the concept of computational fluid dynamics (CFD) modeling with a clinical perspective and we review the CFD models used to study RE from the fluid mechanics point of view. Finally, we show what CFD simulations have taught us about the hemodynamics during RE, the current capabilities of CFD simulations of RE, and we suggest some future perspectives.

Circulating Fluidized Beds

2010 ◽  
pp. 316-358
Author(s):  
Ray Cocco ◽  
S.B. Reddy Karri ◽  
Ted Knowlton
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Large Scale ◽  
Cfd Modeling ◽  
Circulating Fluidized Beds ◽  
Cfd Models ◽  
Unit Operations ◽  
Computational Fluid Dynamics Cfd

In the last 20 years, significant improvements in the computational fluid dynamics (CFD) modeling have been made that allow the simulation of large-scale, commercial CFBs. Today, commercial codes are available that can model some of this behavior in large-scale, commercial units in a reasonable amount of time. However, the hydrodynamics in a riser or fluidized bed are complex with both micro and macroscale features. From particle clustering to large streamers to the core-annulus profile, the particle behavior in these unit operations rarely behaves as a “continuous fluid.” Even the role of particle size distribution is often neglected and models that do consider particle size distribution don’t always consider the role of particle size on granular temperature. Many models use insufficient boundary conditions by assuming uniform or symmetric profiles, which is rarely the case. Furthermore, grid sizing is usually based on computer limitations instead of model limitations, and many models of commercial systems extend beyond the capability of the constitutive equations being used. Successful application of today’s CFD models requires a good understanding of the equations behind the code, the assumptions used for those equations and the capability or limitations of the code. CFD is nothing more than a guess without an understanding of the fundamentals, underlying assumptions and code limitations that are part of every model.

scholarly journals Comparison of Experiments to Computational Fluid Dynamics Models for Mixing Using Dual Opposing Jets in Tanks With and Without Internal Obstructions

2012 ◽  
Vol 134 (11) ◽  
Author(s):  
Robert A. Leishear ◽  
Si Y. Lee ◽  
Mark D. Fowley ◽  
Michael R. Poirier ◽  
Timothy J. Steeper
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Experimental Testing ◽  
Liquid Radioactive Waste ◽  
Correction Factors ◽  
Time Data ◽  
Cfd Models ◽  
New Findings ◽  
Computational Fluid Dynamics Cfd ◽  
Cooling Coils

This paper documents testing methods, statistical data analysis, and a comparison of experimental results to computational fluid dynamics (CFD) models for blending of fluids, which were blended using a single pump designed with dual opposing nozzles in an 8-foot-diameter tank. Overall, this research presents new findings in the field of mixing research. Specifically, blending processes were clearly shown to have random, chaotic effects, where possible causal factors, such as turbulence, pump fluctuations, and eddies, required future evaluation. CFD models were shown to provide reasonable estimates for the average blending times, but large variations—or scatter—occurred for blending times during similar tests. Using this experimental blending time data, the chaotic nature of blending was demonstrated and the variability of blending times with respect to average blending times was shown to increase with system complexity. Prior to this research, the variation in blending times caused discrepancies between CFD models and experiments. This research addressed this discrepancy and determined statistical correction factors that can be applied to CFD models and thereby quantified techniques to permit the application of CFD models to complex systems, such as blending. These blending time correction factors for CFD models are comparable to safety factors used in structural design and compensate variability that cannot be theoretically calculated. To determine these correction factors, research was performed to investigate blending using a pump with dual opposing jets, which recirculate fluids in the tank to promote blending when fluids are added to the tank. In all, 85 tests were performed both in a tank without internal obstructions and a tank with vertical obstructions similar to a tube bank in a heat exchanger. These obstructions provided scale models of vertical cooling coils below the liquid surface for a full-scale, liquid radioactive waste storage tank. Also, different jet diameters and different horizontal orientations of the jets were investigated with respect to blending. Two types of blending tests were performed. The first set of 81 tests blended small quantities of tracer fluids into solution. Data from these tests were statistically evaluated to determine blending times for the addition of tracer solution to tanks, and blending times were successfully compared to computational fluid dynamics (CFD) models. The second set of four tests blended bulk quantities of solutions of different density and viscosity. For example, in one test, a quarter tank of water was added to three quarters of a tank of a more viscous salt solution. In this case, the blending process was noted to significantly change due to stratification of fluids and blending times increased substantially. However, CFD models for stratification and the variability of blending times for different density fluids were not pursued, and further research is recommended in the area of blending bulk quantities of fluids. All in all, testing showed that CFD models can be effectively applied if statistically validated through experimental testing, but, in the absence of experimental validation, CFD models can be extremely misleading as a basis for design and operation decisions.

Recovery boiler sootblowers: History and technological advances

TAPPI Journal ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 51-60
Author(s):  
HONGHI TRAN ◽  
DANNY TANDRA
Keyword(s):  
Cfd Modeling ◽  
Computing Systems ◽  
The Past ◽  
Technological Advances ◽  
Recovery Boilers ◽  
Computational Fluid Dynamics Cfd ◽  
Recovery Boiler ◽  
Steam Parameters ◽  
Insight Into ◽  
Deposit Removal

Sootblowing technology used in recovery boilers originated from that used in coal-fired boilers. It started with manual cleaning with hand lancing and hand blowing, and evolved slowly into online sootblowing using retractable sootblowers. Since 1991, intensive research and development has focused on sootblowing jet fundamentals and deposit removal in recovery boilers. The results have provided much insight into sootblower jet hydrodynamics, how a sootblower jet interacts with tubes and deposits, and factors influencing its deposit removal efficiency, and have led to two important innovations: fully-expanded sootblower nozzles that are used in virtually all recovery boilers today, and the low pressure sootblowing technology that has been implemented in several new recovery boilers. The availability of powerful computing systems, superfast microprocessors and data acquisition systems, and versatile computational fluid dynamics (CFD) modeling capability in the past two decades has also contributed greatly to the advancement of sootblowing technology. High quality infrared inspection cameras have enabled mills to inspect the deposit buildup conditions in the boiler during operation, and helped identify problems with sootblower lance swinging and superheater platens and boiler bank tube vibrations. As the recovery boiler firing capacity and steam parameters have increased markedly in recent years, sootblowers have become larger and longer, and this can present a challenge in terms of both sootblower design and operation.

scholarly journals Computational Investigation of Inclusion Removal in the Steel-Refining Ladle Process

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1048
Author(s):  
Xipeng Guo ◽  
Joel Godinez ◽  
Nicholas J. Walla ◽  
Armin K. Silaen ◽  
Helmut Oltmann ◽  
...  
Keyword(s):  
Multiphase Flow ◽  
Porous Plug ◽  
Cfd Modeling ◽  
Balance Model ◽  
Computational Investigation ◽  
Inclusion Removal ◽  
Inclusion Interaction ◽  
Computational Fluid Dynamics Cfd ◽  
Steel Refining ◽  
Transient Multiphase Flow

In a steel-refining ladle, the properties of manufactured steel can be notably degraded due to the presence of excessive inclusions. Stirring via gas injection through a porous plug is often used as part of the steel-refining process to reduce these inclusions. In this paper, 3D computational fluid dynamics (CFD) modeling is used to analyze transient multiphase flow and inclusion removal in a gas-stirred ladle. The effects of gas stirring with bubble-inclusion interaction are analyzed using the Euler–Euler approach for multiphase flow modeling, while the effects of inclusions aggregation and removal are modeled via a population balance model (PBM).

scholarly journals Performance Prediction of a Hard-Chine Planing Hull by Employing Different CFD Models

2021 ◽  
Vol 9 (5) ◽  
pp. 481
Author(s):  
Azim Hosseini ◽  
Sasan Tavakoli ◽  
Abbas Dashtimanesh ◽  
Prasanta K. Sahoo ◽  
Mihkel Kõrgesaar
Keyword(s):  
Water Flow ◽  
Performance Prediction ◽  
Cfd Modeling ◽  
Computational Time ◽  
Resistance Force ◽  
Stagnation Line ◽  
Mesh Structure ◽  
Cfd Models ◽  
High Speeds ◽  
Des Model

This paper presents CFD (Computational Fluid Dynamics) simulations of the performance of a planing hull in a calm-water condition, aiming to evaluate similarities and differences between results of different CFD models. The key differences between these models are the ways they use to compute the turbulent flow and simulate the motion of the vessel. The planing motion of a vessel on water leads to a strong turbulent fluid flow motion, and the movement of the vessel from its initial position can be relatively significant, which makes the simulation of the problem challenging. Two different frameworks including k-ε and DES (Detached Eddy Simulation) methods are employed to model the turbulence behavior of the fluid motion of the air–water flow around the boat. Vertical motions of the rigid solid body in the fluid domain, which eventually converge to steady linear and angular displacements, are numerically modeled by using two approaches, including morphing and overset techniques. All simulations are performed with a similar mesh structure which allows us to evaluate the differences between results of the applied mesh motions in terms of computation of turbulent air–water flow around the vessel. Through quantitative comparisons, the morphing technique has been seen to result in smaller errors in the prediction of the running trim angle at high speeds. Numerical observations suggest that a DES model can modify the accuracy of the morphing mesh simulations in the prediction of the trim angle, especially at high-speeds. The DES model has been seen to increase the accuracy of the model in the computation of the resistance of the vessel in a high-speed operation, as well. This better level of accuracy in the prediction of resistance is a result of the calculation of the turbulent eddies emerging in the water flow in the downstream zone, which are not captured when a k-ε framework is employed. The morphing approach itself can also increase the accuracy of the resistance prediction. The overset method, however, overpredicts the resistance force. This overprediction is caused by the larger vorticity, computed in the direction of the waves, generated under the bow of the vessel. Furthermore, the overset technique is observed to result in larger hydrodynamic pressure on the stagnation line, which is linked to the greater trim angle, predicted by this approach. The DES model is seen to result in extra-damping of the second and third crests of transom waves as it calculates the stronger eddies in the wake of the boat. Overall, a combination of the morphing and DES models is recommended to be used for CFD modeling of a planing hull at high-speeds. This combined CFD model might be relatively slower in terms of computational time, but it provides a greater level of accuracy in the performance prediction, and can predict the energy damping, developed in the surrounding water. Finally, the results of the present paper demonstrate that a better level of accuracy in the performance prediction of the vessel might also be achieved when an overset mesh motion is used. This can be attained in future by modifying the mesh structure in such a way that vorticity is not overpredicted and the generated eddies, emerging when a DES model is employed, are captured properly.

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